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1. DNN-based semantic rescoring models for speech recognition

Author: Irina Illina, Dominique Fohr, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), The authors thank the DGA (Direction Générale de l’Armement, part of the French Ministry of Defence), Thales AVS and Dassault Aviation who are supporting the funding of this study and the 'Man-Machine Teaming' scientific program., MMT, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Artificial neural network, Computer science, Speech recognition, Automatic speech recognition, Word error rate, 020206 networking & telecommunications, Context (language use), 02 engineering and technology, Semantic data model, Semantics, 01 natural sciences, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Word2vec, Language model, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 010301 acoustics, semantics, embeddings, Word (computer architecture), BERT
Abstract: International audience; In this work, we address the problem of improving an automatic speech recognition (ASR) system. We want to efficiently model long-term semantic relations between words and introduce this information through a semantic model. We propose neural network (NN) semantic models for rescoring the N-best hypothesis list. These models use two types of representations as part of DNN input features: static word embeddings (from word2vec) and dynamic contextual embeddings (from BERT). Semantic information is computed thanks to these representations and used in the hypothesis pair comparison mode. We perform experiments on the publicly available dataset TED-LIUM. Clean speech and speech mixed with real noise are experimented, according to our industrial project context. The proposed BERT-based rescoring approach gives a significant improvement of the word error rate (WER) over the ASR system without rescoring semantic models under all experimented conditions and with n-gram and recurrent NN language model (Long Short-Term model, LSTM).
Published: 2021

2. BERT-based Semantic Model for Rescoring N-best Speech Recognition List

Author: Irina Illina, Dominique Fohr, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), The authors thank the DGA (Direction Générale de l’Armement, part of the French Ministry of Defence), Thales AVS and Dassault Aviation who are supporting the funding of this study and the 'Man-Machine Teaming' scientific program in which this project is taking place., Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Artificial neural network, Computer science, Speech recognition, Automatic speech recognition, Word error rate, 020206 networking & telecommunications, 02 engineering and technology, Semantic data model, Semantic consistency, 030507 speech-language pathology & audiology, 03 medical and health sciences, Noise, semantic context, 0202 electrical engineering, electronic engineering, information engineering, Semantic context, Word2vec, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 0305 other medical science, embeddings, Word (computer architecture), BERT
Abstract: International audience; This work aims to improve automatic speech recognition (ASR) by modeling long-term semantic relations. We propose to perform this through rescoring the ASR N-best hypotheses list. To achieve this, we propose two deep neural network (DNN) models and combine semantic, acoustic, and linguistic information. Our DNN rescoring models are aimed at selecting hypotheses that have better semantic consistency and therefore lower WER. We investigate a powerful representation as part of input features to our DNN model: dynamic contextual embeddings from Transformer-based BERT. Acoustic and linguistic features are also included. We perform experiments on the publicly available dataset TED-LIUM. We evaluate in clean and in noisy conditions, with n-gram and Recurrent Neural Network Language Model (RNNLM), more precisely Long Short-Term Memory (LSTM) model. The proposed rescoring approaches give significant WER improvements over the ASR system without rescoring models. Furthermore, the combination of rescoring methods based on BERT and GPT-2 scores achieves the best results.
Published: 2021
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3. Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection

Author: Irina Illina, Dominique Fohr, Tulika Bose, Bose, Tulika, ISITE - Isite LUE - - LUE2015 - ANR-15-IDEX-0004 - IDEX - VALID, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), This work was supported partly by the french PIA project 'Lorraine Université d’Excellence', reference ANR-15-IDEX-04-LUE., IMPACT-OLKi, GRID5000, ANR-15-IDEX-0004,LUE,Isite LUE(2015), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Domain adaptation, Language identification, Computer science, business.industry, 05 social sciences, 02 engineering and technology, [INFO] Computer Science [cs], computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), Annotation, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, Artificial intelligence, Language model, 0509 other social sciences, 050904 information & library sciences, business, Adaptation (computer science), computer, Natural language processing
Abstract: International audience; The state-of-the-art abusive language detection models report great in-corpus performance, but underperform when evaluated on abusive comments that differ from the training scenario. As human annotation involves substantial time and effort, models that can adapt to newly collected comments can prove to be useful. In this paper, we investigate the effectiveness of several Unsupervised Domain Adaptation (UDA) approaches for the task of cross-corpora abusive language detection. In comparison, we adapt a variant of the BERT model, trained on large-scale abusive comments, using Masked Language Model (MLM) fine-tuning. Our evaluation shows that the UDA approaches result in sub-optimal performance, while the MLM fine-tuning does better in the cross-corpora setting. Detailed analysis reveals the limitations of the UDA approaches and emphasizes the need to build efficient adaptation methods for this task.
Published: 2021

4. Introduction of semantic model to help speech recognition

Author: Stephane Level, Dominique Fohr, Irina Illina, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), The authors thank the DGA (Direction G´en´erale de l’Armement, part of the French Ministry of Defence), Thales AVS and Dassault Aviation who are supporting the funding of this study and the 'Man-Machine Teaming' scientific program in which this research project is taking place., Man-Machine Teaming (MMT), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Computer science, Speech recognition, Automatic speech recognition, Word error rate, Context (language use), 02 engineering and technology, Semantic data model, Term (time), Embeddings, 030507 speech-language pathology & audiology, 03 medical and health sciences, Noise, Semantic context, Similarity (psychology), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Word2vec, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 0305 other medical science, Sentence
Abstract: International audience; Current Automatic Speech Recognition (ASR) systems mainly take into account acoustic, lexical and local syntactic information. Long term semantic relations are not used. ASR systems significantly decrease performance when the training conditions and the testing conditions differ due to the noise, etc. In this case the acoustic information can be less reliable. To help noisy ASR system, we propose to supplement ASR system with a semantic module. This module re-evaluates the N-best speech recognition hypothesis list and can be seen as a form of adaptation in the context of noise. For the words in the processed sentence that could have been poorly recognized, this module chooses words that correspond better to the semantic context of the sentence. To achieve this, we introduced the notions of a context part and possibility zones that measure the similarity between the semantic context of the document and the corresponding possible hypothesis. The proposed methodology uses two continuous representations of words: word2vec and FastText. We conduct experiments on the publicly available TED conferences dataset (TED-LIUM) mixed with real noise. The proposed method achieves a significant improvement of the word error rate (WER) over the ASR system without semantic information.
Published: 2020

5. A Fine-grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

Author: Odile Mella, Karima Abidi, Kamel Smaïli, David Langlois, Denis Jouvet, Dominique Fohr, Statistical Machine Translation and Speech Modelization and Text (SMarT), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Appraisal theory, Word embedding, Arabic, Polarity (physics), Computer science, business.industry, Sentiment analysis, 02 engineering and technology, computer.software_genre, Video analysis, language.human_language, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, language, Sentiment Analysis, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Utterance, Natural language processing, Graduation
Abstract: International audience; The objective of this paper is to compare the opinions of two videos in two different languages. To do so, a fine-grained approach inspired from the appraisal theory is used to analyze the content of the videos that concern the same topic. In general, the methods devoted to sentiment analysis concern the study of the polarity of a text or an utterance. The appraisal approach goes further than the basic polarity sentiments and consider more detailed sentiments by covering additional attributes of opinions such as: Attitude, Graduation and Engagement. In order to achieve such a comparison, in AMIS (Chist-Era project), we collected a corpus of 1503 Arabic and 1874 English videos. These videos need to be aligned in order to compare their contents, that is why we propose several methods to make them comparable. Then the best one is selected to align them and to constitute the data-set necessary for the fine-grained sentiment analysis.
Published: 2019
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6. Extractive Text-Based Summarization of Arabic videos: Issues, Approaches and Evaluations

Author: Juan-Manuel Torres-Moreno, Kamel Smaïli, David Langlois, Denis Jouvet, Odile Mella, Mohamed Amine Menacer, Dominique Fohr, Karima Abidi, Carlos-Emiliano González-Gallardo, Fatiha Sadat, Statistical Machine Translation and Speech Modelization and Text (SMarT), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université du Québec à Montréal = University of Québec in Montréal (UQAM), and Centre d'Enseignement et de Recherche en Informatique - CERI-Avignon Université (AU)
Subjects: Computer science, Arabic, business.industry, Automatic speech recognition ASR, Foreign language, Word error rate, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, Automatic summarization, language.human_language, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Segmentation, Text summarization, 0202 electrical engineering, electronic engineering, information engineering, language, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Scope (computer science), Natural language processing, Video summarisation
Abstract: International audience; In this paper, we present and evaluate a method for extractive text-based summarization of Arabic videos. The algorithm is proposed in the scope of the AMIS project that aims at helping a user to understand videos given in a foreign language (Arabic). For that, the project proposes several strategies to translate and summarize the videos. One of them consists in transcribing the Ara-bic videos, summarizing the transcriptions, and translating the summary. In this paper we describe the video corpus that was collected from YouTube and present and evaluate the transcription-summarization part of this strategy. Moreover, we present the Automatic Speech Recognition (ASR) system used to transcribe the videos, and show how we adapted this system to the Algerian dialect. Then, we describe how we automatically segment into sentences the sequence of words provided by the ASR system, and how we summarize the obtained sequence of sentences. We evaluate objectively and subjectively our approach. Results show that the ASR system performs well in terms of Word Error Rate on MSA, but needs to be adapted for dealing with Algerian dialect data. The subjective evaluation shows the same behaviour than ASR: transcriptions for videos containing dialectal data were better scored than videos containing only MSA data. However, summaries based on transcriptions are not as well rated, even when transcriptions are better rated. Last, the study shows that features, such as the lengths of transcriptions and summaries, and the subjective score of transcriptions, explain only 31% of the subjective score of summaries.
Published: 2019
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7. Summarizing videos into a target language: Methodology, architectures and evaluation

Author: Begona Garcia-Zapirain, Lucjan Janowski, Michał Grega, Odile Mella, David Langlois, Kamel Smaïli, Eric SanJuan, Denis Jouvet, Dominique Fohr, Mikołlaj Leszczuk, Elvys Linhares Pontes, Mohamed-Amine Menacer, Carlos-Emiliano González-Gallardo, Amaia Mendez, Juan-Manuel Torres-Moreno, Arian Koźbiał, Statistical Machine Translation and Speech Modelization and Text (SMarT), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, AGH University of Science and Technology [Krakow, PL] (AGH UST), University of Deusto (DEUSTO), and Universidad de Deusto (DEUSTO)
Subjects: Statistics and Probability, Machine translation, Computer science, media_common.quotation_subject, Foreign language, Text Boundary Segmentation, 02 engineering and technology, Automatic Speech Recognition, computer.software_genre, Speech segmentation, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Artificial Intelligence, Component (UML), 0202 electrical engineering, electronic engineering, information engineering, Quality (business), Objective and Subjective evaluations, media_common, Focus (computing), Information retrieval, Video Summarization, General Engineering, Collecting data, Statistical Machine Translation, Automatic summarization, Text and Audio Summarization, 020201 artificial intelligence & image processing, State (computer science), computer
Abstract: International audience; The aim of the work is to report the results of the Chist-Era project AMIS (Access Multilingual Information opinionS). The purpose of AMIS is to answer the following question: How to make the information in a foreign language accessible for everyone? This issue is not limited to translate a source video into a target language video since the objective is to provide only the main idea of an Arabic video in English. This objective necessitates developing research in several areas that are not, all arrived at a maturity state: Video summarization, Speech recognition, Machine translation, Audio summarization and Speech segmentation. In this article we present several possible architectures to achieve our objective, yet we focus on only one of them. The scientific locks are be presented, and we explain how to deal with them. One of the big challenges of this work is to conceive a way to evaluate objectively a system composed of several components knowing that each of them has its limits and can propagate errors through the first component. Also, a subjective evaluation procedure is proposed in which several annotators have been mobilized to test the quality of the achieved summaries.
Published: 2019
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8. A First Summarization System of a Video in a Target Language

Author: Carlos-Emiliano González-Gallardo, Juan-Manuel Torres-Moreno, Denis Jouvet, Michał Grega, Amaia Mendez, Begona Garcia-Zapirain, Kamel Smaïli, David Langlois, Lucjan Janowski, Artur Komorowski, Damian Świst, Arian Koźbiał, Elvys Linhares Pontes, Odile Mella, Mohamed Amine Menacer, Dominique Fohr, Mikołaj Leszczuk, Eric SanJuan, Statistical Machine Translation and Speech Modelization and Text (SMarT), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, AGH University of Science and Technology [Krakow, PL] (AGH UST), Universidad de Deusto (DEUSTO), École Polytechnique de Montréal (EPM), and University of Deusto (DEUSTO)
Subjects: Machine translation, Computer science, Process (engineering), media_common.quotation_subject, Foreign language, Video Summarization, Compression, 020206 networking & telecommunications, Text Boundary Segmentation ·, 02 engineering and technology, computer.software_genre, Automatic summarization, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], World Wide Web, Speech Recognition, Machine Translation, Fragment (logic), Text summarization, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Aptitude, Content (Freudian dream analysis), computer, media_common, Meaning (linguistics)
Abstract: International audience; In this paper, we present the first results of the project AMIS (Access Multilingual Information opinionS) funded by Chist-Era. The main goal of this project is to understand the content of a video in a foreign language. In this work, we consider the understanding process, such as the aptitude to capture the most important ideas contained in a media expressed in a foreign language. In other words, the understanding will be approached by the global meaning of the content of a support and not by the meaning of each fragment of a video. Several stumbling points remain before reaching the fixed goal. They concern the following aspects: Video summarization, Speech recognition, Machine translation and Speech segmentation. All these issues will be discussed and the methods used to develop each of these components will be presented. A first implementation is achieved and each component of this system is evaluated on a representative test data. We propose also a protocol for a global subjective evaluation of AMIS.
Published: 2018

9. An Integrated AMIS Prototype for Automated Summarization and Translation of Newscasts and Reports

Author: Mohamed Amine Menacer, Denis Jouvet, Juan-Manuel Torres-Moreno, Odile Mella, Michał Grega, Mikołaj Leszczuk, Dominique Fohr, Carlos-Emiliano González-Gallardo, Elvys Linhares Pontes, Kamel Smaïli, AGH University of Science and Technology [Krakow, PL] (AGH UST), Statistical Machine Translation and Speech Modelization and Text (SMarT), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, École Polytechnique de Montréal (EPM), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Kazimierz Choroś, Marek Kopel, Elżbieta Kukla, and Andrzej Siemiński
Subjects: Machine translation, Computer science, business.industry, 0206 medical engineering, 02 engineering and technology, Translation (geometry), computer.software_genre, 020601 biomedical engineering, Automatic summarization, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 03 medical and health sciences, Software modules, 0302 clinical medicine, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Natural language processing
Abstract: International audience; In this paper we present the results of the integration works on the system designed for automated summarization and translation of newscast and reports. We show the proposed system architectures and list the available software modules. Thanks to well defined interfaces the software modules may be used as building blocks allowing easy experimentation with different summarization scenarios.
Published: 2018
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10. Dynamic Extension of ASR Lexicon Using Wikipedia Data

Author: Badr M. Abdullah, Irina Illina, Dominique Fohr, Illina, Irina, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Text corpus, Vocabulary, Word embedding, Computer science, media_common.quotation_subject, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, 02 engineering and technology, [INFO] Computer Science [cs], Semantics, computer.software_genre, Lexicon, 01 natural sciences, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, lexicon extension, Proper noun, [INFO]Computer Science [cs], 010301 acoustics, media_common, Context model, business.industry, Search engine indexing, Automatic speech recognition, word embedding, out-of-vocabulary words, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing
Abstract: International audience; Despite recent progress in developing Large Vocabulary Continuous Speech Recognition Systems (LVCSR), these systems suffer from Out-Of-Vocabulary words (OOV). In many cases, the OOV words are Proper Nouns (PNs). The correct recognition of PNs is essential for broadcast news, audio indexing, etc. In this article, we address the problem of OOV PN retrieval in the framework of broadcast news LVCSR. We focused on dynamic (document dependent) extension of LVCSR lexicon. To retrieve relevant OOV PNs, we propose to use a very large multipurpose text corpus: Wikipedia. This corpus contains a huge number of PNs. These PNs are grouped in semantically similar classes using word embedding. We use a two-step approach: first, we select OOV PN pertinent classes with a multi-class Deep Neural Network (DNN). Secondly, we rank the OOVs of the selected classes. The experiments on French broadcast news show that the Bi-GRU model outperforms other studied models. Speech recognition experiments demonstrate the effectiveness of the proposed methodology.
Published: 2018

11. Topic segmentation in ASR transcripts using bidirectional rnns for change detection

Author: Imran Sehikh, Irina Illina, Dominique Fohr, TCS Innovation Labs, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Grid'5000, ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012), Fohr, Dominique, and BLANC - Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio - - ContNomina2012 - ANR-12-BS02-0009 - BLANC - VALID
Subjects: business.industry, Computer science, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, Cohesion (linguistics), topic segmentation, Recurrent neural network, Discriminative model, Broadcast television systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, The Internet, recurrent neural networks, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, Change detection, Natural language processing
Abstract: International audience; Topic segmentation methods are mostly based on the idea of lexical cohesion, in which lexical distributions are analysed across the document and segment boundaries are marked in areas of low cohesion. We propose a novel approach for topic segmentation in speech recognition transcripts by measuring lexical cohesion using bidirectional Recurrent Neural Networks (RNN). The bidirectional RNNs capture context in the past and the following set of words. The past and following contexts are compared to perform topic change detection. In contrast to existing works based on sequence and discrim-inative models for topic segmentation, our approach does not use a segmented corpus nor (pseudo) topic labels for training. Our model is trained using news articles obtained from the internet. Evaluation on ASR transcripts of French TV broadcast news programs demonstrates the effectiveness of our proposed approach.
Published: 2017

12. Development of the Arabic Loria Automatic Speech Recognition system (ALASR) and its evaluation for Algerian dialect

Author: Kamel Smaïli, Mohamed Amine Menacer, Odile Mella, David Langlois, Denis Jouvet, Dominique Fohr, Statistical Machine Translation and Speech Modelization and Text (SMarT), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Arabic, Computer science, Speech recognition, 02 engineering and technology, computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Algerian dialect, MSA, 0202 electrical engineering, electronic engineering, information engineering, General Environmental Science, business.industry, French, Acoustic model, 020206 networking & telecommunications, language.human_language, Language model, language, Modern Standard Arabic, General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Word (computer architecture), Natural language processing
Abstract: International audience; This paper addresses the development of an Automatic Speech Recognition system for Modern Standard Arabic (MSA) and its extension to Algerian dialect. Algerian dialect is very different from Arabic dialects of the Middle-East, since it is highly influenced by the French language. In this article, we start by presenting the new automatic speech recognition named ALASR (Arabic Loria Automatic Speech Recognition) system. The acoustic model of ALASR is based on a DNN approach and the language model is a classical n-gram. Several options are investigated in this paper to find the best combination of models and parameters. ALASR achieves good results for MSA in terms of WER (14.02%), but it completely collapses on an Algerian dialect data set of 70 minutes (a WER of 89%). In order to take into account the impact of the French language, on the Algerian dialect, we combine in ALASR two acoustic models, the original one (MSA) and a French one trained on ESTER corpus. This solution has been adopted because no transcribed speech data for Algerian dialect are available. This combination leads to a substantial absolute reduction of the word error of 24%. c 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 3rd International Conference on Arabic Computational Linguistics .
Published: 2017

13. Modelling Semantic Context of OOV Words in Large Vocabulary Continuous Speech Recognition

Author: Georges Linarès, Dominique Fohr, Irina Illina, Imran Sheikh, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Centre d'Enseignement et de Recherche en Informatique - CERI-Avignon Université (AU), Grid'5000, ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Topic model, Vocabulary, Acoustics and Ultrasonics, Computer science, media_common.quotation_subject, Speech recognition, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Context (language use), 02 engineering and technology, 010501 environmental sciences, Semantics, computer.software_genre, 01 natural sciences, Latent Dirichlet allocation, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], symbols.namesake, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Electrical and Electronic Engineering, out-of-vocabulary, proper names, 0105 earth and related environmental sciences, media_common, Context model, business.industry, Computational Mathematics, large vocabulary continuous speech recognition, Automatic indexing, semantic context, symbols, 020201 artificial intelligence & image processing, Artificial intelligence, Language model, business, computer, Natural language processing
Abstract: International audience; The diachronic nature of broadcast news data leads to the problem of Out-Of-Vocabulary (OOV) words in Large Vocabulary Continuous Speech Recognition (LVCSR) systems. Analysis of OOV words reveals that a majority of them are Proper Names (PNs). However PNs are important for automatic indexing of audio-video content and for obtaining reliable automatic transcriptions. In this paper, we focus on the problem of OOV PNs in diachronic audio documents. To enable recovery of the PNs missed by the LVCSR system, relevant OOV PNs are retrieved by exploiting the semantic context of the LVCSR transcriptions. For retrieval of OOV PNs, we explore topic and semantic context derived from Latent Dirichlet Allocation (LDA) topic models, continuous word vector representations and the Neural Bag-of-Words (NBOW) model which is capable of learning task specific word and context representations. We propose a Neural Bag-of-Weighted Words (NBOW2) model which learns to assign higher weights to words that are important for retrieval of an OOV PN. With experiments on French broadcast news videos we show that the NBOW and NBOW2 models outperform the methods based on raw embeddings from LDA and Skip-gram models. Combining the NBOW and NBOW2 models gives a faster convergence during training. Second pass speech recognition experiments, in which the LVCSR vocabulary and language model are updated with the retrieved OOV PNs, demonstrate the effectiveness of the proposed context models.
Published: 2017
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14. An enhanced automatic speech recognition system for Arabic

Author: Mohamed Amine Menacer, Kamel Smaïli, Denis Jouvet, Dominique Fohr, Odile Mella, David Langlois, Smaïli, Kamel, Statistical Machine Translation and Speech Modelization and Text (SMarT), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), AMIS - Chist-Era Project, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Arabic, Speech recognition, Arabic Speech recognition, Word error rate, 02 engineering and technology, computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Task (project management), Arabic speech recognition, 0202 electrical engineering, electronic engineering, information engineering, business.industry, 020206 networking & telecommunications, language.human_language, Language model, Focus (linguistics), [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Modern Standard Arabic, language, 020201 artificial intelligence & image processing, Artificial intelligence, Rewriting, business, computer, Natural language processing, Arabic processing
Abstract: International audience; Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition , it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right hamoza above or below Alif.
Published: 2017

15. Learning Word Importance with the Neural Bag-of-Words Model

Author: Imran Sheikh, Georges Linarès, Dominique Fohr, Irina Illina, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012), Fohr, Dominique, and BLANC - Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio - - ContNomina2012 - ANR-12-BS02-0009 - BLANC - VALID
Subjects: business.industry, Computer science, Speech recognition, 02 engineering and technology, 010501 environmental sciences, computer.software_genre, 01 natural sciences, Task (project management), Bag-of-words model, Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, Word (computer architecture), Natural language processing, 0105 earth and related environmental sciences
Abstract: The Neural Bag-of-Words (NBOW) model performs classification with an average of the input word vectors and achieves an impressive performance. While the NBOW model learns word vectors targeted for the classification task it does not explicitly model which words are important for given task. In this paper we propose an improved NBOW model with this ability to learn task specific word importance weights. The word importance weights are learned by introducing a new weighted sum composition of the word vectors. With experiments on standard topic and sentiment classification tasks, we show that (a) our proposed model learns meaningful word importance for a given task (b) our model gives best accuracies among the BOW approaches. We also show that the learned word importance weights are comparable to tf-idf based word weights when used as features in a BOWSVM classifier.
Published: 2016

16. Temporal and Lexical Context of Diachronic Text Documents for Automatic Out-Of-Vocabulary Proper Name Retrieval

Author: Irina Illina, Georges Linarès, Dominique Fohr, Imane Nkairi, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Zygmunt Vetulani, Hans Uszkoreit, Marek Kubis, and ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012)
Subjects: Vocabulary, Out-of-vocabulary words, Computer science, media_common.quotation_subject, Word error rate, Context (language use), 02 engineering and technology, Proper names, Speech recognition, computer.software_genre, Out of vocabulary, Task (project management), 0202 electrical engineering, electronic engineering, information engineering, Selection (linguistics), Proper noun, [INFO]Computer Science [cs], media_common, business.industry, 020206 networking & telecommunications, Key (cryptography), 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing, Vocabulary augmentation
Abstract: International audience; Proper name recognition is a challenging task in information retrieval from large audio/video databases. Proper names are semantically rich and are usually key to understanding the information contained in a document. Our work focuses on increasing the vocabulary coverage of a speech transcription system by automatically retrieving proper names from contemporary diachronic text documents. We proposed methods that dynamically augment the automatic speech recognition system vocabulary using lexical and temporal features in diachronic documents. We also studied different metrics for proper name selection in order to limit the vocabulary augmentation and therefore the impact on the ASR performances. Recognition results show a significant reduction of the proper name error rate using an augmented vocabulary.
Published: 2016
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17. Continuous Word Representation using Neural Networks for Proper Name Retrieval from Diachronic Documents

Author: Dominique Fohr, Irina Illina, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012), Fohr, Dominique, BLANC - Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio - - ContNomina2012 - ANR-12-BS02-0009 - BLANC - VALID, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Vocabulary, Computer science, Speech recognition, media_common.quotation_subject, Word error rate, 02 engineering and technology, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, Word representation, Proper noun, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], proper names, media_common, vocabulary extension, Recall, Artificial neural network, business.industry, speech recognition, 020206 networking & telecommunications, neural networks, out-of-vocabulary words, Artificial intelligence, Transcription (software), [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], 0305 other medical science, business, computer, Natural language processing
Abstract: International audience; Developing high-quality transcription systems for very large vocabulary corpora is a challenging task. Proper names are usually key to understanding the information contained in a document. One approach for increasing the vocabulary coverage of a speech transcription system is to automatically retrieve new proper names from contemporary diachronic text documents. In recent years, neural networks have been successfully applied to a variety of speech recognition tasks. In this paper, we investigate whether neural networks can enhance word representation in vector space for the vocabulary extension of a speech recognition system. This is achieved by using high-quality word vector representation of words from large amounts of unstructured text data proposed by Mikolov. This model allows to take into account lexical and semantic word relationships. Proposed methodology is evaluated in the context of broadcast news transcription. Obtained recall and ASR proper name error rate is compared to that obtained using cosine-based vector space methodology. Experimental results show a good ability of the proposed model to capture semantic and lexical information
Published: 2015

18. Neural Networks for Proper Name Retrieval in the Framework of Automatic Speech Recognition

Author: Dominique Fohr, Irina Illina, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Vocabulary, Artificial neural network, Computer science, business.industry, Speech recognition, media_common.quotation_subject, speech recognition, Context (language use), computer.software_genre, neural networks, out-of-vocabulary words, Proper noun, Speech analytics, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business, Representation (mathematics), computer, Word (computer architecture), Natural language processing, proper names, media_common, vocabulary extension
Abstract: International audience; The problem of out-of-vocabulary words, more precisely proper names retrieval for in speech recognition is investigated. Speech recognition vocabulary is extended using diachronic documents. This article explores a new method based on neural network (NN), proposed recently by Mikolov. The NN uses high-quality continuous representation of words from large amounts of unstructured text data and predicts surrounding words of one input word. Different strategies of using the NN to take into account lexical context are proposed. Experimental results on broadcast speech recognition and comparison with previously proposed methods show an ability of NN representation to model semantic and lexical context of proper names.
Published: 2015

19. Recognition of OOV Proper Names in Diachronic Audio News

Author: Imran Sheikh, Dominique Fohr, Irina Illina, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), ANR-12-BS02-0009,ContNomina,Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio(2012), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Fohr, Dominique, and BLANC - Exploitation du contexte pour la reconnaissance de noms propres dans les documents diachroniques audio - - ContNomina2012 - ANR-12-BS02-0009 - BLANC - VALID
Subjects: Topic model, Text corpus, business.industry, Computer science, Speech recognition, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Search engine indexing, computer.software_genre, Latent Dirichlet allocation, symbols.namesake, Phonetic search technology, symbols, Proper noun, The Internet, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, Natural language processing, ComputingMilieux_MISCELLANEOUS
Abstract: LVCSR based audio indexing approaches are preferred as they allow search, navigation, browsing and structuring of audio/video documents based on their content. A major challenge with LVCSR based indexing of diachronic audio data, for e.g. broadcast audio news, is OOV words and specifically OOV PNs which are very important for indexing applications. In this paper we propose an approach for recognition of OOV PNs in audio news documents using PNs extracted from collections of diachronic text news from the internet. The approach has two steps (a) reduce the long list of OOV PNs in the diachronic text corpus to a smaller list of OOV PNs which are relevant to the audio document, using probabilistic topic models (b) perform a phonetic search for the target OOV PNs with the reduced list of relevant OOV PNs. We evaluate our approach on French broadcast news videos published over a period of 6 months. Latent Dirichlet Allocation topic model is trained on diachronic text news to model PN-topic relationships and then to retrieve OOV PNs relevant to the audio document. Our proposed method retrieves up to 90% of the relevant OOV PNs by reducing the OOV PN search space to only 5% of the total OOV PNs. Phonetic search for target OOV PNs gives an F1-score up to 0.392.
Published: 2015

20. Constitution d'un Corpus de Français Langue Etrangère destiné aux Apprenants Allemands

Author: Odile Mella, Anne Bonneau, Dominique Fohr, Camille Fauth, Vincent Colotte, Yves Laprie, Jürgen Trouvain, Denis Jouvet, Analysis, perception and recognition of speech (PAROLE), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Allgemeine Linguistik Computational Linguistics and phonetics (Allgemeine Linguistik), Saarland University [Saarbrücken], ANR IFCASL, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: French, phonétique, phonetics, allemand, parole non-native, français, 02 engineering and technology, German, [SCCO.LING]Cognitive science/Linguistics, language learning, corpus de parole, lcsh:Social Sciences, lcsh:H, apprentissage des langues, 030507 speech-language pathology & audiology, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], 0305 other medical science, non native speech corpora
Abstract: La plupart des corpus en langue se focalisent sur les phénomènes linguistiques écrits et concernent l’anglais (voir le site web : « Learner corpora around the world » de l’Université de Louvain - Belgique). La recherche phonétique sur l’acquisition d’une L2 est généralement orientée vers l’étude des phénomènes segmentaux et la plupart des études ont également l’anglais comme langue cible. Les modèles de parole en L2 actuels - voir par exemple Speech Learning Model (Flege, 1995) ou Best’s Perceptual Assimilation Model (Best, 1995) – négligent bien souvent les aspects prosodiques. Notre étude concerne le français en tant que langue seconde et s’inscrit dans un projet plus vaste mené en partenariat avec une université allemande, dont l’un des buts est le développement de l’apprentissage des langues par ordinateur. (Projet ANR-DFG – Agence Nationale de la Recherche et Deutsche Forschungsgemeinschaft attribué à l’équipe Parole du LORIA UMR 7503, Nancy – France et à l’Equipe de Linguistique Computationnelle et de Phonétique FR 4.7 de l’Université de la Sarre Sarrebruck – Allemagne) dans lequel le français et l’allemand sont des langues cibles. Pour la paire allemand-français, peu de corpus parallèles sont disponibles. Nous présentons ici l’élaboration d’un corpus de productions orales de locuteurs natifs et non natifs pour la paire allemand-français. Notre corpus entend mettre au jour les déviations phonétiques et phonologiques que les locuteurs allemands produisent lorsqu’ils apprennent le français. Ce travail s’insère dans un projet plus global, Ce projet entend étudier les difficultés que les locuteurs français rencontrent lorsqu’ils apprennent l’allemand, et réciproquement. Aussi, cinquante locuteurs allemands seront recrutés dans des milieux universitaires et scolaires (niveau lycée) en Allemagne et cinquante locuteurs français dans les mêmes milieux en France. Il s’agit pour les deux populations de produire d’une part le corpus en langue étrangère (en langue française pour les locuteurs allemands et en langue allemande pour les locuteurs français) mais également le corpus en langue maternelle (en allemand pour les allemands et en français pour les français). Les corpus ainsi obtenus devraient nous permettre d’identifier les difficultés que les locuteurs allemands ou français rencontrent lorsqu’ils apprennent le français ou l’allemand. Les données de contrôle sont doubles puisque l’on pourra à la fois se référer aux productions des apprenants dans leur langue maternelle (ici l’allemand), mais également à celles de locuteurs natifs (ici germanophones). Nous ne présenterons ici que la constitution du corpus en français.
Published: 2014
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21. Combining Forward-based and Backward-based Decoders for Improved Speech Recognition Performance

Author: Denis Jouvet, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Jouvet, Denis, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Speech recognition, Word error rate, 020206 networking & telecommunications, 02 engineering and technology, Set (abstract data type), 030507 speech-language pathology & audiology, 03 medical and health sciences, Speech recognition performance, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 0305 other medical science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Test data
Abstract: International audience; Combining outputs of speech recognizers is a known way of increasing speech recognition performance. The ROVER approach handles efficiently such combinations. In this paper we show that the best performance is not achieved by combining the outputs of the best set of recognizers, but rather by combining outputs of recognizers that rely on different processing components, and in particular on a different order (backward vs. forward) for processing speech frames. Indeed, much better speech recognition results were obtained by combining outputs of sphinx-based recognizers with outputs of Julius-based recognizers than by combining the same number of outputs from only sphinx-based recognizers, even if the individual sphinx-based systems led to better results than the individual Julius-based recognizers. Further experiments have also been conducted using sphinx-based tools for processing speech frames in reverse order (i.e. backward in time). The results clearly show that combining forward-based and backward-based decoders provide significant improvement with respect to a combination of forward only or backward only decoders. Experiments have been conducted on the ESTER2 and ETAPE speech corpora. Overall, combining sphinx-based and Julius-based systems led to 18.6% word error rate on ESTER2 test data, and 24.5% word error rate on ETAPE test data.
Published: 2013

22. Combination of Random Indexing based Language Model and N-gram Language Model for Speech Recognition

Author: Dominique Fohr, Odile Mella, Analysis, perception and recognition of speech (PAROLE), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: language modeling, Perplexity, business.industry, Latent semantic analysis, Computer science, Speech recognition, speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Function (mathematics), computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, Random indexing, n-gram, Cache language model, random indexing, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, Language model, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 0305 other medical science, business, computer, Natural language processing
Abstract: International audience; This paper presents the results and conclusion of a study on the introduction of semantic information through the Random Indexing paradigm in statistical language models used in speech recognition. Random Indexing is an alternative to Latent Semantic Analysis (LSA) that addresses the scalability problem of LSA. After a brief presentation of Random Indexing (RI), this paper describes, different methods to estimate the RI matrix, then how to derive probabilities from the RI matrix and finally how to combine them with n-gram language model probabilities. Then, it analyzes the performance of these different RI methods and their combinations with a 4-gram language model by computing the perplexity of a test corpus of 290,000 words from the French evaluation campaign ETAPE. Among our results, the main conclusions are (1) regardless of the method, function words should not be taken into account in the estimation of RI matrix; (2) The two methods RI_basic and TTRI_w achieved the best perplexity, i.e. a relative gain of 3% compared to the perplexity of the 4-gram language model alone (136.2 vs. 140.4).
Published: 2013

23. Combining criteria for the detection of incorrect entries of non-native speech in the context of foreign language learning

Author: Anne Bonneau, Dominique Fohr, Irina Illina, Luiza Orosanu, Denis Jouvet, Analysis, perception and recognition of speech (PAROLE), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), and Jouvet, Denis
Subjects: incorrect entries, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Speech recognition, Chinese speech synthesis, Speech coding, Speech synthesis, Pronunciation, computer.software_genre, 01 natural sciences, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0103 physical sciences, Speech analytics, Foreign language learning, 010301 acoustics, 030304 developmental biology, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Cued speech, 0303 health sciences, business.industry, Speech corpus, Speech processing, non-native speech, constrained and unconstrained alignments, Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Natural language processing
Abstract: International audience; This article analyzes the detection of incorrect entries of nonnative speech in the context of foreign language learning. The purpose is to detect and reject incorrect entries (i.e. those for which the speech signal does not correspond at all to the associated text) while being tolerant to the mispronunciations of non-native speech. The proposed approach exploits the comparison between two text-to-speech alignments : one constrained by the text which is being checked, with another one unconstrained, corresponding to a phonetic decoding. Several comparison criteria are described and combined via a logistic regression function. The article analyzes the influence of different settings, such as the impact of non-native pronunciation variants, the impact of learning the decision functions on native or on non-native speech, as well as the impact of combining various comparison criteria. The performance evaluations are conducted both on native and on non-native speech.
Published: 2012

24. Multilingual Recognition of Non-Native Speech using Acoustic Model Transformation and Pronunciation Modeling

Author: Irina Illina, Dominique Fohr, Ghazi Bouselmi, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Fohr, Dominique
Subjects: Linguistics and Language, Computer science, Speech recognition, First language, Non-native pronunciations of English, Pronunciation, computer.software_genre, 01 natural sciences, Language and Linguistics, 030507 speech-language pathology & audiology, 03 medical and health sciences, 0103 physical sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Hidden Markov model, 010301 acoustics, business.industry, Acoustic model, Speech corpus, Human-Computer Interaction, Transformation (function), Computer Vision and Pattern Recognition, Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], 0305 other medical science, business, computer, Software, Natural language processing, Spoken language
Abstract: International audience; This article presents an approach for the automatic recognition of non-native speech. Some non-native speakers tend to pronounce phonemes as they would in their native language. Model adaptation can improve the recognition rate for non-native speakers, but has difficulties dealing with pronunciation errors like phoneme insertions or substitutions. For these pronunciation mismatches, pronunciation modeling can make the recognition system more robust. Our approach is based on acoustic model transformation and pronunciation modeling for multiple non-native accents. For acoustic model transformation, two approaches are evaluated: MAP and model re-estimation. For pronunciation modeling, confusion rules (alternate pronunciations) are automatically extracted from a small non-native speech corpus. This paper presents a novel approach to introduce confusion rules in the recognition system which are automatically learned through pronunciation modelling. The modified HMM of a foreign spoken language phoneme includes its canonical pronunciation along with all the alternate non-native pronunciations, so that spoken language phonemes pronounced correctly by a non-native speaker could be recognized. We evaluate our approaches on the European project HIWIRE non-native corpus which contains English sentences pronounced by French, Italian, Greek and Spanish speakers. Two cases are studied: the native language of the test speaker is either known or unknown. Our approach gives better recognition results than the classical acoustic adaptation of HMM when the foreign origin of the speaker is known. We obtain 22% WER reduction compared to the reference system. Furthermore, we take into account the written form of the spoken words: non-native speakers may rely on the writing of the words in order to pronounce them. This approach does not provide any further improvements.
Published: 2012

25. Evaluating grapheme-to-phoneme converters in automatic speech recognition context

Author: Dominique Fohr, Irina Illina, Denis Jouvet, Analysis, perception and recognition of speech (PAROLE), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Conditional random field, business.industry, Computer science, Speech recognition, Grapheme, speech recognition, 020206 networking & telecommunications, Context (language use), 02 engineering and technology, Pronunciation, Lexicon, computer.software_genre, Grapheme-to-phoneme, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, pronunciation lexicon, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Precision and recall, Hidden Markov model, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Natural language processing
Abstract: International audience; This paper deals with the evaluation of grapheme-to-phoneme (G2P) converters in a speech recognition context. The precision and recall rates are investigated as potential measures of the quality of the multiple generated pronunciation variants. Very different results are obtained whether or not we take into account the frequency of occurrence of the words. Since G2P systems are rarely evaluated on a speech recognition performance basis, the originality of this paper consists in using a speech recognition system to evaluate the G2P pronunciation variants. The results show that the training process is quite robust to some errors in the pronunciation lexicon, whereas pronunciation lexicon errors are harmful in the decoding process. Noticeable speech recognition performance improvements are achieved by combining two different G2P converters, one based on conditional random fields and the other on joint multigram models, as well as by checking the pronunciation variants of the most frequent words.
Published: 2012
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26. Grapheme-to-Phoneme Conversion using Conditional Random Fields

Author: Denis Jouvet, Dominique Fohr, Irina Illina, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), International Speech Communication Association (ISCA) et The Italian Regional SIG - AISV (Italian Speech Communication Association), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Jouvet, Denis
Subjects: Conditional random field, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Computer science, Bigram, Speech recognition, Grapheme, 020206 networking & telecommunications, Context (language use), Pattern recognition, 02 engineering and technology, Pronunciation, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, 0305 other medical science, business, Precision and recall, Hidden Markov model, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Independence (probability theory), [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; We propose an approach to grapheme-to-phoneme conversion based on a probabilistic method: Conditional Random Fields (CRF). CRF give a long term prediction, assume relaxed state independence condition. Moreover, we propose an algorithm to one-to-one letter to phoneme alignment needed for CRF training. This alignment is based on discrete HMM. The proposed system is validated on two pronunciation dictionaries. Different CRF features are studied: POS-tag, context size, unigram versus bigram. Our approach compares favorably with the performance of the state-of-the-art Joint-Multigram Models for the quality of the pronunciations, but provides better recall and precision measures for multiple pronunciation variants generation.
Published: 2011

27. Frame-Synchronous and Local Confidence Measures for Automatic Speech recognition

Author: Odile Mella, Jean-Paul Haton, Joseph Razik, Dominique Fohr, Université de Toulon (UTLN), Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)
Subjects: likelihood ratio, Modalities, Computer science, Low Confidence, Speech recognition, Posterior probability, Frame (networking), speech recognition, 020206 networking & telecommunications, 02 engineering and technology, frame-synchronous measure, Measure (mathematics), posterior probability, Comprehension, Artificial Intelligence, Confidence measure, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Transcription (software), Software
Abstract: International audience; In this paper, we introduce two new confidence measures for large vocabulary speech recognition systems. The major feature of these measures is that they can be computed without waiting for the end of the audio stream. We proposed two kinds of confidence measures: frame-synchronous and local. The frame-synchronous ones can be computed as soon as a frame is processed by the recognition engine and are based on a likelihood ratio. The local measures estimate a local posterior probability in the vicinity of the word to analyze. We evaluated our confidence measures within the framework of the automatic transcription of French broadcast news with the EER criterion. Our local measures achieved results very close to the best state-of-the-art measure (EER of 23% compared to 22.0%). We then conducted a preliminary experiment to assess the contribution of our confidence measure in improving the comprehension of an automatic transcription for the hearing impaired. We introduced several modalities to highlight words of low confidence in this transcription. We showed that these modalities used with our local confidence measure improved the comprehension of automatic transcription
Published: 2011
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28. A wavelet-based parameterization for speech/music discrimination

Author: Emmanuel Didiot, Irina Illina, Odile Mella, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Frequency band, Computer science, Speech recognition, Word error rate, Speech/music discrimination, 02 engineering and technology, Wavelets, Theoretical Computer Science, Static parameters, Reduction (complexity), 030507 speech-language pathology & audiology, 03 medical and health sciences, Wavelet, Segmentation, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Signal processing, Human-Computer Interaction, Sound recording and reproduction, Computer Science::Sound, Dynamic parameters, 020201 artificial intelligence & image processing, Mel-frequency cepstrum, 0305 other medical science, Long-term parameters, Software, Energy (signal processing)
Abstract: International audience; This paper addresses the problem of parameterization for speech/music discrimination. The current successful parameterization based on cepstral coefficients uses the Fourier transformation (FT), which is well adapted for stationary signals. In order to take into account the non-stationarity of music/speech signals, this work proposes to study wavelet-based signal decomposition instead of FT. Three wavelet families and several numbers of vanishing moments have been evaluated. Different types of energy, calculated for each frequency band obtained from wavelet decomposition, are studied. Static, dynamic and long-term parameters were evaluated. The proposed parameterization are integrated into two class/non-class classifiers: one for speech/non-speech, one for music/non-music. Different experiments on realistic corpora, including different styles of speech and music (Broadcast News, Entertainment, Scheirer), illustrate the performance of the proposed parameterization, especially for music/non-music discrimination. Our parameterization yielded a significant reduction of the error rate. More than 30% relative improvement was obtained for the envisaged tasks compared to MFCC parameterization.
Published: 2010
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29. Frame-Synchronous and Local Confidence Measures for on-the-fly Automatic Speech Recognition

Author: Joseph Razik, Odile Mella, Dominique Fohr, Jean-Paul Haton, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 030507 speech-language pathology & audiology, 03 medical and health sciences, likelihood ratio, conﬁdence measures, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020206 networking & telecommunications, 02 engineering and technology, Speech recognition, 0305 other medical science, posterior probability
Abstract: International audience; This paper presents several new conﬁdence measures with the major advantage that they can be evaluated as soon as possible without having to wait for the recognition process to be completed. We have deﬁned two kinds of conﬁdence measures. The ﬁrst one can be computed synchronously with the frame processed by the engine and the second one with a slight delay. Such measures are useful for driving the recognition process by modifying the likelihood score or for validating recognised words in on-the-ﬂy applications such as keyword spotting task and on-line automatic speech transcription for deaf people. The EER evaluation on a French broadcast news corpus shows a performance close to the batch version of these measures (23.0% against 22.0% of EER) with only 0.84s of data before and after the word to be analysed.
Published: 2008

30. Foreign accent identification based on prosodic parameters

Author: Irina Illina, Dominique Fohr, Marina Piat, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Fohr, Dominique
Subjects: [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Speech recognition, 02 engineering and technology, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Stress (linguistics), 0202 electrical engineering, electronic engineering, information engineering, Duration (project management), Prosody, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, prosodic parameters, business.industry, foreign accent identification, 020206 networking & telecommunications, non-native speech, Identification (information), Mel-frequency cepstrum, Artificial intelligence, 0305 other medical science, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Natural language processing
Abstract: International audience; In this paper we propose an automatic approach for foreign accent identification. Knowledge of the speaker's origin allows to adapt acoustic models for non-native speech recognition. In this study, we use a statistical approach based on prosodic parameters. This approach relies on the fact that prosody is different between languages, and has been done within the framework of the HIWIRE (Human Input that Works In Real Environments) European project. The corpus is composed of English sentences pronounced by French, Italian, Greek and Spanish speakers. Results obtained with duration and energy are promising for foreign accent identification: 67.1% correct L1 identification with duration and 68.6% with energy. These two parameters combined with MFCC achieve a 87.1% correct foreign accent identification rate.
Published: 2008

31. On-line Stochastic Matching compensation for non-stationary noise

Author: Irina Illina, Dominique Fohr, V. Barreaud, Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Computer science, Word error rate, 02 engineering and technology, Theoretical Computer Science, Compensation (engineering), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Cepstrum, 0202 electrical engineering, electronic engineering, information engineering, Value noise, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS, Noise measurement, business.industry, 020206 networking & telecommunications, Pattern recognition, White noise, Human-Computer Interaction, Gradient noise, Noise, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Artificial intelligence, 0305 other medical science, business, Algorithm, Software
Abstract: This paper treats the problem of noise compensation in speech recognition when training and testing conditions do not match. We are interested in two types of non-stationary noise that may be present during test, namely slowly varying and abruptly varying noises. The context of our work is the Stochastic Matching framework. The Stochastic Matching compensation method transforms test data using an affine compensation function whose parameters are computed off-line. Stochastic Matching approaches are interesting since they make little assumptions about the nature and the level of the noise but they are best suited for the compensation of stationary noise. In this paper we propose an original contribution to the Stochastic Matching framework. It is based on an on-line frame-synchronous version of Stochastic Matching method to compensate for slowly varying noise. Our contribution extends this compensation algorithm in order to compensate for abruptly varying noise. The basic idea of the proposed methods is to perform the compensation and the recognition steps at the same time. The environment changes are identified using monitoring algorithms. The performance of our proposed methods is evaluated on two speech databases, one recorded in moving cars (VODIS), and another one obtained by corrupting VODIS with abruptly varying noise from NOISEX. The proposed approaches significantly outperform classical compensation methods (Off-line Stochastic Matching, Sequential Mean Cepstrum Removal, Parallel Model Combination, Spectral Subtraction). For instance, we obtain up to 32.6% word error rate reduction over S-MCR on database corrupted by a 10dB abruptly varying white noise.
Published: 2008
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32. Dynamic Gaussian Selection Technique for Speeding Up HMM-Based Continuous Speech Recognition

Author: Ghazi Bouselmi, Jun Cai, Dominique Fohr, Yves Laprie, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Fohr, Dominique
Subjects: [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Gaussian, Speech recognition, fast likelihood computation, TIMIT, 02 engineering and technology, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov model, Gaussian process, Selection (genetic algorithm), [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, business.industry, hidden Markov models, speech recognition, 020206 networking & telecommunications, Pattern recognition, Gaussian selection, Computer Science::Sound, symbols, Artificial intelligence, State (computer science), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; A fast likelihood computation approach called dynamic Gaussian selection (DGS) is proposed for HMM-based continuous speech recognition. DGS approach is a one-pass search technique which generates a dynamic shortlist of Gaussians for each state during the procedure of likelihood computation. The shortlist consists of the Gaussians which make prominent contribution to the likelihood. In principle, DGS is an extension of the technique of Partial Distance Elimination, and it requires almost no additional memory for the storage of Gaussian shortlists. DGS algorithm has been implemented by modifying the likelihood computation module in HTK 3.4 system. Results from experiments on TIMIT and HIWIRE corpora indicate that this approach can speed up the likelihood computation significantly without introducing apparent additional recognition error.
Published: 2008

33. Speaker Diarization using Normalized Cross Likelihood Ratio

Author: Odile Mella, Dominique Fohr, Viet Bac Le, Fohr, Dominique, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Normalization (statistics), business.industry, Computer science, Gaussian, Speech recognition, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Viterbi algorithm, Measure (mathematics), Hierarchical clustering, Speaker diarisation, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, 0202 electrical engineering, electronic engineering, information engineering, symbols, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], 0305 other medical science, business, Change detection
Abstract: International audience; In this paper, we present the Normalized Cross Likelihood Ratio (NCLR) and the advantages of using it in a speaker diarization system. First, the NCLR is used as a dissimilarity measure between two Gaussian speaker models in the speaker change detection step and its contribution to the performance of speaker change detection is compared with those of BIC and Hostelling's T2-Statistic measures. Then, the NCLR measure is modified to deal with multi-gaussian adapted models in the cluster recombination step. This step ends the step-by-step speaker diarization process after the BIC-based hierarchical clustering and the Viterbi re-segmentation steps. By comparing the NCLR measure with the CLR (Cross Likelihood Ratio) one, more than 30% of relative diarization error is reduced in ESTER evaluation data.
Published: 2007

34. Optimizing the coverage of a speech database through a selection of representative speaker recordings

Author: Olivier Boëffard, Frédéric Bimbot, Delphine Charlet, Sacha Krstulovic, Dominique Fohr, Odile Mella, Speech and sound data modeling and processing (METISS), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Human-machine spoken dialogue (CORDIAL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-INRIA Rennes, Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT), France Télécom Recherche & Développement (FT R&D), France Télécom, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-INRIA Rennes
Subjects: Linguistics and Language, speaker selection, Computer science, speech and speaker recognition, media_common.quotation_subject, Speech recognition, Context (language use), Speech synthesis, 02 engineering and technology, computer.software_genre, Language and Linguistics, 030507 speech-language pathology & audiology, 03 medical and health sciences, Similarity (network science), cost minimization, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Cluster analysis, Selection (genetic algorithm), speech database, media_common, speaker clustering, multi-models, optimal coverage, Database, Communication, Variety (linguistics), Similitude, Computer Science Applications, Modeling and Simulation, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Data mining, 0305 other medical science, computer, Software
Abstract: International audience; In the context of the Neologos French speech database creation project, we have defined a general methodology for the selection of representative speaker recordings. The selection aims at insuring a good coverage in terms of speaker variability while limiting the number of recorded speakers. This makes the resulting database both more adapted to the development of recently proposed multi-model methods and cheaper to collect. The presented methodology proposes to operate a selection by optimizing a quality criterion defined in a variety of speaker similarity modeling frameworks. The selection can be operated and validated with respect to a unique similarity criterion, using classical clustering methods such as Hierarchical or K-Medians clustering, or it can be operated and validated across several speaker similarity criteria, thanks to a newly developed clustering method called Focal Speakers Selection. In this framework, four different speaker similarity criteria are tested, and three different speaker clustering algorithms are compared. Results pertaining to the collection of the Neologos database are also discussed.
Published: 2006
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35. Multilingual Non-Native Speech Recognition using Phonetic Confusion-Based Acoustic Model Modification and Graphemic Constraints

Author: Irina Illina, Jean-Paul Haton, Dominique Fohr, Ghazi Bouselmi, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Ghazi, Bouselmi
Subjects: Computer science, First language, Speech recognition, Word error rate, 02 engineering and technology, pronunciation modelling, Pronunciation, computer.software_genre, Lexicon, 01 natural sciences, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, non-native speech recognition, 010301 acoustics, business.industry, Acoustic model, 020207 software engineering, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Artificial intelligence, multilingual, business, computer, Word (computer architecture), Sentence, Natural language processing, HMM modification, Spoken language
Abstract: In this paper we present an automated approach for non-native speech recognition. We introduce a new phonetic confusion concept that associates sequences of native language (NL) phones to spoken language (SL) phones. Phonetic confusion rules are automatically extracted from a non-native speech database for a given NL and SL using both NL's and SL's ASR systems. These rules are used to modify the acoustic models (HMMs) of SL's ASR by adding acoustic models of NL's phones according to these rules. As pronunciation errors that non-native speakers produce depend on the writing of the words, we have also used graphemic constraints in the phonetic confusion extraction process. In the lexicon, the phones in words' pronunciations are linked to the corresponding graphemes (characters) of the word. In this way, the phonetic confusion is established between couples of (SL phones, graphemes) and sequences of NL phones. We evaluated our approach on French, Italian, Spanish and Greek non-native speech databases. The spoken language is English. The modified ASR system achieved significant improvements ranging from 20.3% to 43.2% (relative) in sentence error rate and from 26.6% to 50.0% in WER.
Published: 2006

36. Fully Automated Non-Native Speech Recognition Using Confusion-Based Acoustic Model Integration And Graphemic Constraints

Author: Irina Illina, Jean-Paul Haton, Dominique Fohr, Ghazi Bouselmi, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)
Subjects: Computer science, Speech recognition, First language, Grapheme, pronunciation modelling, Pronunciation, computer.software_genre, 01 natural sciences, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 030507 speech-language pathology & audiology, 03 medical and health sciences, non-native, Phone, HMM model modification, 0103 physical sciences, Hidden Markov model, 010301 acoustics, business.industry, Confusion matrix, Acoustic model, Artificial intelligence, 0305 other medical science, business, computer, Natural language, Natural language processing, Spoken language
Abstract: This paper presents a fully automated approach for the recognition of non-native speech based on acoustic model modification. For a native language (L1) and a spoken language (L2), pronunciation variants of the phones of L2 are automatically extracted from an existing non-native database as a confusion matrix with sequences of phones of L1. This is done using LTs and L2's ASR systems. This confusion concept deals with the problem of non existence of match between some L2 and L1 phones. The confusion matrix is then used to modify the acoustic models (HMMs) of L2 phones by integrating corresponding L1 phone models as alternative HMM paths. We introduce graphemic constraints in the confusion extraction process: the phonetic confusion is established for each couple of 'L2-phone' and the grapheme(s) corresponding to that phone. We claim that pronunciation errors may depend on the graphemes related to each phone. The modified ASR system achieved an improvement between 32% and 40% (relative, L1=French and L2=English) in WER on the French non-native database used for testing. The introduction of graphemic constraints in the phonetic confusion allowed further improvements
Published: 2006

37. Neologos: an optimized database for the development of new speech processing algorithms

Author: Sacha Krstulovic, Frédéric Bimbot, Olivier Boëffard, Delphine Charlet, Odile Mella, Khalid Choukri, Djamel Mostefa, Arnaud Vallée, Dominique Fohr, Filip Korkmazsky, France Télécom Recherche & Développement (FT R&D), France Télécom, Speech and sound data modeling and processing (METISS), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Human-machine spoken dialogue (CORDIAL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-INRIA Rennes, Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT), Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Evaluations and Language resources Distribution Agency (ELDA), ELDA, TELISMA (TELISMA), TELISMA, Fohr, Dominique, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-INRIA Rennes
Subjects: Audio mining, Computer science, Speech synthesis, 02 engineering and technology, computer.software_genre, Field (computer science), [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 030507 speech-language pathology & audiology, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, Speech analytics, speech database, speaker clustering, Database, business.industry, Speech technology, speech recognition, French, 020206 networking & telecommunications, Speech corpus, Speech processing, language.human_language, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], language, Artificial intelligence, 0305 other medical science, business, computer, Algorithm, Natural language processing
Abstract: The Neologos project is a speech database creation project for the French language, resulting from a collaboration between universities and industrial companies and supported by the French Ministry of Research. The goal of Neologos is to re-think the design of the speech databases in order to enable the development of new algorithms in the field of speech processing. A general method is proposed to optimize the database contents in terms of diversity of the recorded voices, while reducing the number of recorded speakers.
Published: 2005

38. Fully Automated Non-Native Speech Recognition Using Confusion-Based Acoustic Model Integration

Author: Jean-Paul Haton, Ghazi Bouselmi, Dominique Fohr, Irina Illina, Ghazi, Bouselmi, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Speech recognition, First language, non native speech, 02 engineering and technology, Pronunciation, Lexicon, computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 030507 speech-language pathology & audiology, 03 medical and health sciences, Phone, HMM structure modification, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov model, business.industry, Acoustic model, Confusion matrix, 020206 networking & telecommunications, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Artificial intelligence, 0305 other medical science, business, computer, Natural language processing, Spoken language
Abstract: This paper presents a fully automated approach for the recognition of non-native speech based on acoustic model modification. For a native language (L1) and a spoken language (L2), pronunciation variants of the phones of L2 are automatically extracted from an existing non-native database as a confusion matrix with sequences of phones of L1. This is done using L1's and L2's ASR systems. This confusion concept deals with the problem of non existence of match between some L2 and L1 phones. The confusion matrix is then used to modify the acoustic models (HMMs) of L2 phones by integrating corresponding L1 phone models as alternative HMM paths. In this way, no lexicon modification is carried. The modified ASR system achieved an improvement between 32% and 40% (relative, L1=French and L2=English) in WER on the French non-native database used for testing.
Published: 2005

39. Hidden Factor Dynamic Bayesian Networks for Speech Recognition

Author: Murat Deviren, Filipp Korkmazsky, Dominique Fohr, Irina Illina, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 02 engineering and technology, Set (abstract data type), 030507 speech-language pathology & audiology, 03 medical and health sciences, Phone, Factor (programming language), 0202 electrical engineering, electronic engineering, information engineering, Cluster analysis, Hidden Markov model, Dynamic Bayesian network, computer.programming_language, reconnaissance de la parole, business.industry, speech recognition, 020206 networking & telecommunications, Pattern recognition, Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Sound, Artificial intelligence, 0305 other medical science, business, computer
Abstract: Colloque avec actes et comité de lecture. nationale.; National audience; This paper presents a novel approach to modeling speech data by Dynamic Bayesian Networks. Instead of defining a specific set of factors that affect speech signals the factors are modeled implicitly by speech data clustering. Different data clusters correspond to different subsets of the factor values. These subsets are represented by the corresponding factor states. The factor states along with the phone states represent 2 hidden layers in the Hidden Factor Dynamic Bayesian Network (HFDBN). In this study we proved that Hidden Factor Dynamic Bayesian Networks provide a better speech recognition performance than HMMs of equal complexity. Speech recognition experiments were conducted on the speech data recorded in a moving car and demonstrated advantage of using HFDBN over HMM for clean and noisy speech data recognition.
Published: 2004

40. Using Linear Interpolation to Improve Histogram Equalization for Speech Recognition

Author: Dominique Fohr, Irina Illina, Filipp Korkmazsky, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Normalization (statistics), Computer science, Color normalization, Speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Normalization (image processing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Linear interpolation, 030507 speech-language pathology & audiology, 03 medical and health sciences, Histogram, 0202 electrical engineering, electronic engineering, information engineering, histogram equalization for speech recogntion, Histogram equalization, égalisation d'histogramme pour la reconnaissance de la parole, business.industry, Histogram matching, 020206 networking & telecommunications, Pattern recognition, Weighting, Adaptive histogram equalization, Artificial intelligence, 0305 other medical science, business, Interpolation
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; This paper presents a novel approach to speech data normalization by introducing interpolation for histogram equalization. We study different ways of histogram interpolation that inhence this normalization technique. We found that using a special weighting factor to combine current and past test sentence statistics improved speech recognition performance.
Published: 2004

41. Un système de compréhension automatique de la parole pour l'interrogation orale d'une base de données de bourse

Author: Salma Jamoussi, Kamel Smaïli, Dominique Fohr, Jean-Paul Haton, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Loria, Publications
Subjects: [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], réseaux bayésiens, classification non supervisée, cutoclass, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], mesures sémantiques, compréhension de la parole, représentation vectorielle des mots, extraction automatique des concepts, autoclass
Abstract: Colloque avec actes et comité de lecture. nationale.; National audience; In this work, we present a complete speech understanding system based on our speech recognizer: ESPERE. The input signal is processed and the best sentence is then proposed to the understanding module. In our case, the understanding problem is considered as a matching process between two different languages. At the entry, the request expressed in natural language and at the output the corresponding SQL form. The SQL request is obtained after an intermediate step in which the entry is expressed in terms of concepts. A concept represents a given meaning, it is defined by a set of words sharing the same semantic properties. In this paper, we propose a new Bayesian classifier to automatically extract the underlined concepts. We also propose a new approach for vector representation of words. Then, we describe the postprocessing step during which, we label our sentences and we generate the corresponding SQL queries. We conclude our paper by describing the integration step of our understanding module in a complete platform of human-machine oral intercation.
Published: 2004

42. Exploiting models intrinsic robustness for noisy speech recognition

Author: Christophe Cerisara, Odile Mella, Dominique Fohr, Irina Illina, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)
Subjects: reconnaissance de la parole, Computer science, business.industry, Speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], speech recognition, Noise compensation, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, robustness, Missing data, robustesse, 030507 speech-language pathology & audiology, 03 medical and health sciences, missing data, Robustness (computer science), Computer Science::Sound, Cepstrum, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, 0305 other medical science, business, données manquantes
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; We propose in this paper an original approach to build masks in the framework of missing data recognition. The proposed soft masks are estimated from the models themselves, and not from the test signal as it is usually the case. They represent the intrinsic robustness of model's log-spectral coefficients. The method is validated with cepstral models, on two synthetic and two real-life noises, at different signal-to-noise ratios. We further discuss how such masks can be combined with other signal-based masks and noise compensation techniques.
Published: 2004

43. Experiments on the accuracy of phone models and liaison processing in a French broadcast news transcription system

Author: Christophe Cerisara, Odile Mella, Irina Illina, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, business.industry, Speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], French, 020206 networking & telecommunications, 02 engineering and technology, language.human_language, 030507 speech-language pathology & audiology, 03 medical and health sciences, Phone, reconnaissance de la parole de nouvelles émissions, broadcast news speech recognition, 0202 electrical engineering, electronic engineering, information engineering, language, Transcription (software), 0305 other medical science, business, Radio broadcasting
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; In the framework of ESTER, the recent French broadcast radio news transcription task evaluation, we have developed the first version of ANTS, the Automatic News Transcription System of LORIA. This paper describes the different components of the ANTS system and provides some first recognition results on the ESTER database. Then it presents several experiments carried out on this system to take into account the specificities of the French language: how accurate should the phones models be and how to deal with the problem of the liaisons between words.
Published: 2004

44. A New Keyword Spotting Approach Based on Reward Function

Author: Jean-Paul Haton, Yassine Benayed, Gérard Chollet, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), Laboratoire du traitement du signal et instrumentation (TSI), Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien [Saint Etienne] (LHC), and Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, media_common.quotation_subject, Speech recognition, modèles de markov cachés, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 02 engineering and technology, Markov model, Viterbi algorithm, 01 natural sciences, mots hors-vocabualire, symbols.namesake, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov model, 010301 acoustics, media_common, Grammar, business.industry, Maximum-entropy Markov model, hidden markov models, 020206 networking & telecommunications, Pattern recognition, Mixture model, Speech processing, filler model, modèle poubelle, out-of-vocabulary words, ComputingMethodologies_PATTERNRECOGNITION, Keyword spotting, symbols, Artificial intelligence, gmm, business
Abstract: Colloque avec actes et comité de lecture. nationale.; National audience; In this paper, we compare the performance achieved by different word-spotting techniques based on hidden Markov models. We propose two methods to detect keywords, the first one uses a GMM (Gaussian Mixture Model) as a filler model to absorb the out-of-vocabulary words. The second is an alternative approach which does not attempt to model out-of-vocabulary words, instead, it uses buckled phonemes basedgrammar. Furthermore, it uses different reward functions to favourite the recognition of the keywords phonemes.
Published: 2003

45. Audio Indexing on the Web: a Preliminary Study of Some Audio Descriptors

Author: Nathalie Parlangeau-Vallès, Jérôme Farinas, Dominique Fohr, Irina Illina, Ivan Magrin-Chagnolleau, Odile Mella, Julien Pinquier, Jean-Luc ROUAS, Christine Sénac, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), Équipe Structuration, Analyse et MOdélisation de documents Vidéo et Audio (IRIT-SAMoVA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Dynamique Du Langage (DDL), Université Lumière - Lyon 2 (UL2)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), N Callaos, International Institute of Informatics and Sysemics, and IIIS
Subjects: segmentation parole/musique, keyword detection, speech/music detection, [INFO.INFO-WB]Computer Science [cs]/Web, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, suivi de locuteurs, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, speaker tracking, audio-content indexing, détection de mots clés, indexation sonore
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; The "Invisible Web" is composed of documents which can not be currently accessed by Web search engines, because they have a dynamic URL or are not textual, like video or audio documents. For audio documents, one solution is automatic indexing. It consists in finding good descriptors of audio documents which can be used as indexes for archiving and search. This paper presents an overview and recent results of the RAIVES project, a French research project on audio indexing. We present speech/music segmentation, speaker tracking, and keywords detection. We also give a few perspectives of the RAIVES project.
Published: 2003

46. On-Line Frame-Synchronous Compensation of Non-Stationary noise

Author: Dominique Fohr, V. Barreaud, Irina Illina, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Loria, Publications
Subjects: Computer science, speech, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Word error rate, 02 engineering and technology, Viterbi algorithm, 01 natural sciences, compensation, symbols.namesake, non-stationaire, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Value noise, 010301 acoustics, business.industry, non-stationary, 020206 networking & telecommunications, Pattern recognition, Function (mathematics), [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Gradient noise, appariement stochastique, Noise, parole, Gaussian noise, symbols, Affine transformation, Artificial intelligence, hmm, stochastic matching, business
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; We present a frame-synchronous noise compensation algorithm that uses Stochastic Matching approach to cope with time-varying unknown noise. This method proposes to estimate simple mapping function in parallel with Viterbi alignment. The technique is entirely general since no assumption is made on the nature, level and variation of noise. Our algorithm is evaluated on the VODIS database recorded in a moving car. For various tasks, our technique outperforms significantly classical methods. For instance, using the affine transformation the proposed algorithm gives an error rate improvement of 13.3% compared to Parallel Model Combination (PMC), 15.5% on Spectral Subtraction (SS) and 27.8% on frame-synchronous Mean Cepstre Removal (MCR) for the numbers recognition task in real noise.
Published: 2003

47. On-line compensation for non-stationary noise

Author: V. Barreaud, Dominique Fohr, Irina Illina, Loria, Publications, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Speech recognition, Gaussian, speech, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 02 engineering and technology, 01 natural sciences, Compensation (engineering), compensation, 010104 statistics & probability, symbols.namesake, 0202 electrical engineering, electronic engineering, information engineering, Control chart, Pattern matching, 0101 mathematics, business.industry, Stochastic process, non-stationary, 020206 networking & telecommunications, Pattern recognition, non stationnaire, Function (mathematics), Speech processing, [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Noise, parole, symbols, Artificial intelligence, business
Abstract: Colloque avec actes et comité de lecture. nationale.; National audience; An improvement of a previously proposed frame-synchronous noise compensation algorithm is presented. The previous algorithm used Stochastic Matching approach and was designed to cope with time-varying noise. The improved version of this algorithm takes into account the abrupt changes in the acoustical environment. The environment change is detected using the Shewart Control Charts detection algorithm that searches for the changes in the means of Gaussian sequence. For each noisy environment a specific mismatch function is estimated. A simple bias is used as mismatch function. After detection of environment change, the bias is reinitialized by the value corresponding to the closest noisy environment previously observed. This technique is general since no assumption is made on the nature, level and occurring time of noise. Our algorithm is evaluated on the VODIS database (speech in moving car). All the test sentences were corrupted by an additive noise occurring at the middle of each sentence. This latest version of our algorithm gives significant improvement compared to classical compensation algorithms.
Published: 2003

48. Dynamic Bayesian Networks for multi-band automatic speech recognition

Author: Christophe Antoine, Khalid Daoudi, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Reconnaissance de la parole, Computer science, Formalism (philosophy), Speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Probabilistic logic, Contrast (statistics), 02 engineering and technology, 01 natural sciences, Réseaux bayésiens, Field (computer science), Theoretical Computer Science, Domain (software engineering), Human-Computer Interaction, Multi band, Bayesian networks, Computer Science::Sound, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 010301 acoustics, Software, Dynamic Bayesian network, Decoding methods
Abstract: Article dans revue scientifique avec comité de lecture.; This paper presents a new approach to multi-band automatic speech recognition which has the advantage to overcome many limitations of classical muti-band systems. The principle of this new approach is to build a speech model in the time-frequency domain using the formalism of dynamic Bayesian networks. In contrast to classical multi-band modeling, this formalism leads to a probabilistic speech model which allows communications between the different sub-bands and, consequently, no recombination step is required in recognition. We develop efficient learning and decoding algorithms both for isolated and continuous speech recognition. We present illustrative experiments on isolated and connected digit recognition tasks. These experiments show that the this new approach is very promising in the field of noisy speech recognition.
Published: 2003
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49. Structural State-Based Frame Synchronous Compensation

Author: Vincent Barreaud, Irina Illina, Dominique Fohr, Filipp Korkmazsky, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Loria, Publications
Subjects: [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], robust speech recognition, structural compensation, compensation structurelle, robuste, reconnaissance automatique de la parole, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], stochastic matching
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; In this paper we present improvements of a frame-synchronous noise compensation algorithm that uses Stochastic Matching approach to cope with time-varying unknown noise. We propose to estimate a hierarchical mapping function in parallel with Viterbi alignment. The structure of the transformation tree is build from the states of acoustical models. The objective of this hierarchical transformation is to better compensate non-linear distortions of the feature space. The technique is entirely general since no assumption is made on the nature, level and variation of noise. Our algorithm is evaluated on the VODIS database recorded in a moving car. For various tasks, proposed technique significantly outperforms classical compensation/adaptation methods.
Published: 2003

50. Keyword Spotting using Support Vector Machines

Author: Dominique Fohr, Yassine Ben Ayed, Gérard Chollet, Jean-Paul Haton, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire du traitement du signal et instrumentation (TSI), Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien [Saint Etienne] (LHC), and Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: reconnaissance de la parole, Computer science, business.industry, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], speech recognition, Pattern recognition, 02 engineering and technology, keywords, support vector machines, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, mots clés, 020204 information systems, Statistical learning theory, Keyword spotting, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Radial basis function, Artificial intelligence, business
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising.
Published: 2002

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