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1. Explaining Deep Learning Models for Speech Enhancement

Author: Sunit Sivasankaran, Emmanuel Vincent, Dominique Fohr, Microsoft Corporation [Redmond], Microsoft Corporation [Redmond, Wash.], 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 made with the support of the French National Research Agency, in the framework of the project VOCADOM 'Robust voice command adapted to the user and to the context for AAL' (ANR-16-CE33-0006). Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as otherorganizations (see https://www.grid5000.fr)., ANR-16-CE33-0006,VOCADOM,Commande vocale robuste adaptée à la personne et au contexte pour l'autonomie à domicile(2016), 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, business.industry, Computer science, Deep learning, Speech recognition, Word error rate, 020206 networking & telecommunications, Context (language use), 02 engineering and technology, explainable AI, Speech enhancement, 030507 speech-language pathology & audiology, 03 medical and health sciences, Noise, feature attribution, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Robustness (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), speech enhancement, Artificial intelligence, 0305 other medical science, business
Abstract: International audience; We consider the problem of explaining the robustness of neural networks used to compute time-frequency masks for speech enhancement to mismatched noise conditions. We employ the Deep SHapley Additive exPlanations (DeepSHAP) feature attribution method to quantify the contribution of every timefrequency bin in the input noisy speech signal to every timefrequency bin in the output time-frequency mask. We define an objective metric-referred to as the speech relevance scorethat summarizes the obtained SHAP values and show that it correlates with the enhancement performance, as measured by the word error rate on the CHiME-4 real evaluation dataset. We use the speech relevance score to explain the generalization ability of three speech enhancement models trained using synthetically generated speech-shaped noise, noise from a professional sound effects library, or real CHiME-4 noise. To the best of our knowledge, this is the first study on neural network explainability in the context of speech enhancement.
Published: 2021
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2. 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

3. Multiword Expression Features for Automatic Hate Speech Detection

Author: Nicolas Zampieri, Dominique Fohr, and Irina Illina
Subjects: Voice activity detection, Artificial neural network, Computer science, business.industry, Deep learning, computer.software_genre, Multiword expression, Word2vec, Artificial intelligence, business, Encoder, computer, Sentence, Word (computer architecture), Natural language processing
Abstract: The task of automatically detecting hate speech in social media is gaining more and more attention. Given the enormous volume of content posted daily, human monitoring of hate speech is unfeasible. In this work, we propose new word-level features for automatic hate speech detection (HSD): multiword expressions (MWEs). MWEs are lexical units greater than a word that have idiomatic and compositional meanings. We propose to integrate MWE features in a deep neural network-based HSD framework. Our baseline HSD system relies on Universal Sentence Encoder (USE). To incorporate MWE features, we create a three-branch deep neural network: one branch for USE, one for MWE categories, and one for MWE embeddings. We conduct experiments on two hate speech tweet corpora with different MWE categories and with two types of MWE embeddings, word2vec and BERT. Our experiments demonstrate that the proposed HSD system with MWE features significantly outperforms the baseline system in terms of macro-F1.
Published: 2021
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4. BERT and fastText Embeddings for Automatic Detection of Toxic Speech

Author: Ashwin Geet D'Sa, 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), Grid'5000, and D'Sa, Ashwin Geet
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], word embeddings, 0209 industrial biotechnology, Computer science, business.industry, Natural language processing, Offensive, 02 engineering and technology, 16. Peace & justice, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, deep neural networks, hate speech detection, Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, Deep neural networks, 020201 artificial intelligence & image processing, Social media, The Internet, Artificial intelligence, business, computer, Word (computer architecture)
Abstract: International audience; With the expansion of Internet usage, catering to the dissemination of thoughts and expressions of an individual, there has been an immense increase in the spread of online hate speech. Social media, community forums, discussion platforms are few examples of common playground of online discussions where people are freely allowed to communicate. However, the freedom of speech may be misused by some people by arguing aggressively, offending others and spreading verbal violence. As there is no clear distinction between the terms offensive, abusive, hate and toxic speech, in this paper we consider the above mentioned terms as toxic speech. In many countries, online toxic speech is punishable by the law. Thus, it is important to automatically detect and remove toxic speech from online medias. Through this work, we propose automatic classification of toxic speech using embedding representations of words and deep-learning techniques. We perform binary and multi-class classification using a Twitter corpus and study two approaches: (a) a method which consists in extracting of word embeddings and then using a DNN classifier; (b) fine-tuning the pre-trained BERT model. We observed that BERT fine-tuning performed much better. Proposed methodology can be used for any other type of social media comments.
Published: 2020
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5. RNN Language Model Estimation for Out-of-Vocabulary Words

Author: Dominique Fohr and Irina Illina
Subjects: Perplexity, Artificial neural network, business.industry, Probability estimation, Computer science, 02 engineering and technology, computer.software_genre, Lexicon, Out of vocabulary, 030507 speech-language pathology & audiology, 03 medical and health sciences, Recurrent neural network, 0202 electrical engineering, electronic engineering, information engineering, Proper noun, 020201 artificial intelligence & image processing, Language model, Artificial intelligence, 0305 other medical science, business, computer, Natural language processing
Abstract: One important issue of speech recognition systems is Out-of Vocabulary words (OOV). These words, often proper nouns or new words, are essential for documents to be transcribed correctly. Thus, they must be integrated in the language model (LM) and the lexicon of the speech recognition system. This article proposes new approaches to OOV proper noun probability estimation using Recurrent Neural Network Language Model (RNNLM). The proposed approaches are based on the notion of closest in-vocabulary (IV) words (list of brothers) to a given OOV proper noun. The probabilities of these words are used to estimate the probabilities of OOV proper nouns thanks to RNNLM. Three methods for retrieving the relevant list of brothers are studied. The main advantages of the proposed approaches are that the RNNLM is not retrained and the architecture of the RNNLM is kept intact. Experiments on real text data from the website of the Euronews channel show relative perplexity reductions of about 14% compared to baseline RNNLM.
Published: 2020
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6. 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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7. 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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8. Machine Translation on a Parallel Code-Switched Corpus

Author: Odile Mella, David Langlois, Kamel Smaïli, Mohamed Amine Menacer, 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), 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), 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: Neural Machine Translation, 050101 languages & linguistics, Machine translation, Computer science, Carry (arithmetic), 02 engineering and technology, computer.software_genre, Translation (geometry), [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Code (semiotics), Parallel code-switched corpora, Resource (project management), Code-switching, Phenomenon, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, business.industry, 05 social sciences, Statistical Machine Translation, 16. Peace & justice, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing, Utterance
Abstract: International audience; Code-switching (CS) is the phenomenon that occurs when a speaker alternates between two or more languages within an utterance or discourse. In this work, we investigate the existence of code-switching in formal text, namely proceedings of multilingual institutions. Our study is carried out on the Arabic-English code-mixing in a parallel corpus extracted from official documents of United Nations. We build a parallel code-switched corpus with two reference translations one in pure Arabic and the other in pure English. We also carry out a human evaluation of this resource in the aim to use it to evaluate the translation of code-switched documents. To the best of our knowledge, this kind of corpora does not exist. The one we propose is unique. This paper examines several methods to translate code-switched corpus: conventional statistical machine translation, the end-to-end neural machine translation and multitask-learning.
Published: 2019
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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. Neural Networks Revisited for Proper Name Retrieval from Diachronic Documents

Author: Irina Illina and Dominique Fohr
Subjects: Vocabulary, Word embedding, Artificial neural network, Recall, business.industry, Computer science, media_common.quotation_subject, computer.software_genre, Transcription (linguistics), Proper noun, Artificial intelligence, business, computer, Natural language processing, media_common
Abstract: 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. To increase the vocabulary coverage, a huge amount of text data should be used. In this paper, we extend the previously proposed neural networks for word embedding models: word vector representation proposed by Mikolov is enriched by an additional non-linear transformation. This model allows to better take into account lexical and semantic word relationships. In the context of broadcast news transcription and in terms of recall, experimental results show a good ability of the proposed model to select new relevant proper names.
Published: 2018
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11. 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

12. 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

13. 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

14. 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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15. 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

16. Dynamic adjustment of language models for automatic speech recognition using word similarity

Author: Anna Currey, 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), 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: word embeddings, language modeling, Vocabulary, Perplexity, Computer science, media_common.quotation_subject, Speech recognition, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Word error rate, 02 engineering and technology, computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Data modeling, ASR, lexicon extension, 0202 electrical engineering, electronic engineering, information engineering, Proper noun, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], media_common, Context model, business.industry, OOV, 020206 networking & telecommunications, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], 020201 artificial intelligence & image processing, Language model, Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, Word (computer architecture), Natural language processing
Abstract: International audience; Out-of-vocabulary (OOV) words can pose a particular problem for automatic speech recognition (ASR) of broadcast news. The language models (LMs) of ASR systems are typically trained on static corpora, whereas new words (particularly new proper nouns) are continually introduced in the media. Additionally, such OOVs are often content-rich proper nouns that are vital to understanding the topic. In this work, we explore methods for dynamically adding OOVs to language models by adapting the n-gram language model used in our ASR system. We propose two strategies: the first relies on finding in-vocabulary (IV) words similar to the OOVs, where word embeddings are used to define similarity. Our second strategy leverages a small contemporary corpus to estimate OOV probabilities. The models we propose yield improvements in perplexity over the baseline; in addition, the corpus-based approach leads to a significant decrease in proper noun error rate over the baseline in recognition experiments.
Published: 2016
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17. 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

18. Document level semantic context for retrieving OOV proper names

Author: Irina Ulina, Georges Linarès, Dominique Fohr, 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), 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), 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), 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: Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Context (language use), 02 engineering and technology, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, Document level, Phonetic search technology, 0202 electrical engineering, electronic engineering, information engineering, Semantic context, Proper noun, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], proper names, Information retrieval, Training set, business.industry, Search engine indexing, OOV, semantic, 020201 artificial intelligence & image processing, Artificial intelligence, 0305 other medical science, business, computer, Natural language processing, indexing
Abstract: International audience; Recognition of Proper Names (PNs) in speech is important for content based indexing and browsing of audio-video data.However, many PNs are Out-Of-Vocabulary (OOV) words nfor LVCSR systems used in these applications due to the diachronicnature of data. By exploiting semantic context of the audio, relevant OOV PNs can be retrieved and then the target PNs can be recovered. To retrieve OOV PNs, we propose to represent their context with document level semantic vectors; and show that this approach is able to handle less frequent OOV PNs in the training data. We study different representations, including Random Projections, LSA, LDA, Skip-gram, CBOW and GloVe. A further evaluation of recovery of target OOV PNs using a phonetic search shows that document level semantic context is reliable for recovery of OOV PNs.
Published: 2016
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19. 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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20. Different word representations and their combination 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), 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), 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, 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, LDA, Computer science, Speech recognition, media_common.quotation_subject, computer.software_genre, Latent Dirichlet allocation, Document level, symbols.namesake, Transcription (linguistics), Word representation, Proper noun, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], proper names, vocabulary extension, media_common, Context model, Artificial neural network, business.industry, speech recognition, neural networks, out-of-vocabulary words, symbols, Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, Natural language processing
Abstract: International audience; This paper deals with the problem of high-quality transcription systems for very large vocabulary automatic speech recognition (ASR). We investigate the problem of automatic retrieval of out-of-vocabulary (OOV) proper names (PNs). We want to take into account the temporal, syntactic and semantic context of words. Nowadays, Artificial Neural Networks (NN) are widely used in natural language processing: continuous space representations of words is learned automatically from unstructured text data. To model the latent topics at document level, Latent Dirichlet Allocation (LDA) has been successful. In this paper, we propose OOV PN retrieval using (1) temporal versus topic context modeling; (2) different word representation spaces for word-level and document-level context modeling; (3) combinations of retrieval results. Experimental evaluation on broadcast news data shows that the proposed method combinations lead to better results. This confirms the complementarity of methods.
Published: 2015
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21. 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

22. OOV Proper Name retrieval using topic and lexical context models

Author: Imran Sheikh, Georges Linarès, Dominique Fohr, and Irina Illina
Subjects: Topic model, Vocabulary, Context model, Computer science, business.industry, media_common.quotation_subject, Speech recognition, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Search engine indexing, Context (language use), computer.software_genre, Keyword spotting, Selection (linguistics), Proper noun, Artificial intelligence, business, computer, Natural language processing, media_common
Abstract: Retrieving Proper Names (PNs) specific to an audio document can be useful for vocabulary selection and OOV recovery in speech recognition, as well as in keyword spotting and audio indexing tasks. We propose methods to infer and retrieve OOV PNs relevant to an audio news document by using probabilistic topic models trained over diachronic text news. LVCSR hypothesis on the audio news document is analysed for latent topics, which is then used to retrieve relevant OOV PNs. Using an LDA topic model we obtain Recall up to 0.87 and Mean Average Precision (MAP) of 0.26 with only top 10% of the retrieved OOV PNs. We further propose methods to re-score and retrieve rare OOV PNs, and a lexical context model to improve the target OOV PN rankings assigned by the topic model, which may be biased due to prominence of certain news events. Re-scoring rare OOV PNs improves Recall whereas the lexical context model improves MAP.
Published: 2015
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23. 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

24. 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

25. About combining forward and backward-based decoders for selecting data for unsupervised training of acoustic models

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), 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), Jouvet, Denis, 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: [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Speech recognition, Word error rate, 02 engineering and technology, 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, unsupervised training, Adaptation (computer science), Selection (genetic algorithm), Measure (data warehouse), business.industry, speech recognition, Process (computing), Training (meteorology), 020206 networking & telecommunications, Pattern recognition, LVCSR, combining recognizer outputs, data selection, Artificial intelligence, Transcription (software), 0305 other medical science, business, Word (computer architecture)
Abstract: International audience; This paper introduces the combination of speech decoders for selecting automatically transcribed speech data for unsupervised training or adaptation of acoustic models. Here, the combination relies on the use of a forward-based and a backward-based decoder. Best performance is achieved when selecting automatically transcribed data (speech segments) that have the same word hypotheses when processed by the Sphinx forward-based and the Julius backward-based transcription systems, and this selection process outperforms confidence measure based selection. Results are reported and discussed for adaptation and for full training from scratch, using data resulting from various selection processes, whether alone or in addition to the baseline manually transcribed data. Overall, selecting automatically transcribed speech segments that have the same word hypotheses when processed by the Sphinx forward-based and Julius backward-based recognizers, and adding this automatically transcribed and selected data to the manually transcribed data leads to significant word error rate reductions on the ESTER2 data when compared to the baseline system trained only on manually transcribed speech data.
Published: 2014
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26. 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

27. 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

28. 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

29. 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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30. KNOWLEDGE-BASED TECHNIQUES IN ACOUSTIC-PHONETIC DECODING OF SPEECH: INTEREST AND LIMITATIONS

Author: Yves Laprie, Dominique Fohr, and Jean-Paul Haton
Subjects: Knowledge representation and reasoning, Process (engineering), Computer science, business.industry, computer.software_genre, Model-based reasoning, Abductive reasoning, Expert system, Knowledge-based systems, Artificial Intelligence, Computer Vision and Pattern Recognition, Artificial intelligence, Software architecture, Set (psychology), business, computer, Software, Natural language processing
Abstract: A major step in the process of speech understanding is the acoustic-phonetic decoding which can be defined as the automatic mapping of the continuous speech wave into a set of predetermined linguistic units such as phones, diphones, syllables, etc. This paper relates to the approach of this problem which consists in exploiting an explicit description of all kinds of available knowledge about the speech communication phenomena, in the general framework of an artificial intelligence knowledge-based system. We will first recall the main difficulties of acoustic-phonetic decoding with a practical example. We will then present the APHODEX system that we have been designing for the past eight years, in terms of software architecture and of knowledge representation and reasoning. The practical evaluation of this system will then be carried out at the different levels of feature extraction, segmentation and labelling. Finally, we will discuss the limitations of our approach and present the ongoing effort to overcome these limitations, especially through the use of abductive reasoning.
Published: 1994
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31. 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

32. Detection of OOV words by combining acoustic confidence measures with linguistic features

Author: Dominique Fohr, Irina Illina, Frederik Stouten, 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: Vocabulary, Computer science, Speech recognition, media_common.quotation_subject, Feature extraction, confidence measures, 02 engineering and technology, Lexicon, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, Rule-based machine translation, Transcription (linguistics), 0202 electrical engineering, electronic engineering, information engineering, Detection theory, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], media_common, Artificial neural network, Grammar, business.industry, OOV, 020206 networking & telecommunications, LVCSR, Linguistics, Artificial intelligence, [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 describes the design of an Out-Of- Vocabulary words (OOV) detector. Such a system is assumed to detect segments that correspond to OOV words (words that are not included in the lexicon) in the output of a LVCSR system. The OOV detector uses acoustic confidence measures that are derived from several systems: a word recognizer constrained by a lexicon, a phone recognizer constrained by a grammar and a phone recognizer without constraints. On top of that it also uses some linguistic features. The experimental results on a French broadcast news transcription task showed that for our approach precision equals recall at 35%
Published: 2009
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33. JTrans: an open-source software for semi-automatic text-to-speech alignment

Author: Dominique Fohr, Christophe Cerisara, Odile Mella, 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 Cerisara, Christophe
Subjects: Audio mining, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Speech recognition, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Speech synthesis, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Transcription (linguistics), Speech analytics, 060201 languages & linguistics, business.industry, speech recognition, Acoustic model, alignment, Speech corpus, 06 humanities and the arts, Transcriber, Speech processing, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, JTrans, 0602 languages and literature, Artificial intelligence, Transcription (software), 0305 other medical science, business, computer, Natural language processing
Abstract: International audience; Aligning speech corpora with text transcriptions is an important requirement of many speech processing, data mining applications and linguistic researches. Despite recent progress in the field of speech recognition, many linguists still manually align spontaneous and noisy speech recordings to guarantee a good alignment quality. This work proposes an open-source java software with an easy-to-use GUI that integrates dedicated semiautomatic speech alignment algorithms that can be dynamically controlled and guided by the user. The objective of this software is to facilitate and speed up the process of creating and aligning speech corpora.
Published: 2009
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34. 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

35. 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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36. Transcribing Southern Min speech corpora with a Web-Based language learning system

Author: Jun Cai, Jacques Feldmar, Jean-Paul Haton, 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), 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: Vocabulary, Computer science, speech, media_common.quotation_subject, Speech recognition, corpora, 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, Transcription (linguistics), 0103 physical sciences, Web application, 010301 acoustics, media_common, business.industry, automatic speech recognition, transcribing, Speech corpus, Speech processing, Language acquisition, Knowledge acquisition, ComputingMethodologies_PATTERNRECOGNITION, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Artificial intelligence, transcription, 0305 other medical science, business, computer, Natural language, Natural language processing
Abstract: The paper proposes a human-computation-based scheme for transcribing Southern Min speech corpora. The core idea is to implement a Web-based language learning system to collect orthographic and phonetic labels from a large amount of language learners and choose the commonly input labels as the transcriptions of the corpora. It is essentially a technology of distributed knowledge acquisition. Some computer-aided mechanisms are also used to verify the collected transcriptions. The benefit of the scheme is that it makes the transcribing task neither tedious nor costly. No significant budget should be made for transcribing large corpora. The design of a system for transcribing Min Nan speech corpora is described in detail. The application of a prototype version of the system shows that this transcribing scheme is an effective and economical way to generate orthographic and phonetic transcriptions.
Published: 2008
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37. 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

38. Frame-synchronous and local confidence measures for on-the-fly keyword spotting

Author: Dominique Fohr, Joseph Razik, Jean-Paul Haton, Odile Mella, Razik, Joseph, 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, Word processing, 02 engineering and technology, keyword spotting, computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, local, 0202 electrical engineering, electronic engineering, information engineering, business.industry, Frame (networking), Process (computing), 020206 networking & telecommunications, [INFO.INFO-SD] Computer Science [cs]/Sound [cs.SD], confidence measure, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Keyword spotting, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], frame synchronous, Artificial intelligence, Transcription (software), 0305 other medical science, business, computer, Natural language processing, Sentence, Decoding methods
Abstract: ISBN : 978-1-4244-0778-1; International audience; This paper presents several new confidence measures for speech recognition applications. The major advantage of these measures is that they can be evaluated with only a part of the whole sentence. Two of these measures can be computed directly within the first step of the recognition process, synchronously with the decoding engine. Such measures are useful to drive the recognition process by modifying the likelihood score or to validate recognized words in on-the-fly applications as keyword spotting task and on-line automatic speech transcription for deaf people. Two kinds of results are given. Firstly, an EER evaluation on a French broadcast news corpus shows performance close to the batch version of these measures (23.9% against 23.8% of EER). Secondly, for the keyword spotting application, our best measure provides a decrease of the false-acceptation rate by 50% with only a decrease of the correct words by 5%.
Published: 2007
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39. Discriminative phoneme sequence extraction for non-native speaker’s origin classification

Author: Dominique Fohr, Jean-Paul Haton, Ghazi Bouselmi, and Irina Illina
Subjects: Sequence, Probabilistic classification, Computer science, business.industry, Speech recognition, Feature extraction, Pattern recognition, Speech processing, Speaker recognition, Reduction (complexity), Discriminative model, Phone, Artificial intelligence, business
Abstract: In this paper we present an automated method for the classification of the origin of non-native speakers. The origin of non-native speakers could be identified by a human listener based on the detection of typical pronunciations for each nationality. Thus we suppose the existence of several phoneme sequences that might allow the classification of the origin of non-native speakers. Our new method is based on the extraction of discriminative sequences of phonemes from a non-native English speech database. These sequences are used to construct a probabilistic classifier for the speakerspsila origin. The existence of discriminative phone sequences in non-native speech is a significant result of this work. The system that we have developed achieved a significant correct classification rate of 96.3% and a significant error reduction compared to some other tested techniques.
Published: 2007
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40. 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

41. Combined Acoustic and Pronunciation Modelling for Non-Native Speech Recognition

Author: 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), 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 Universiteit antwerpen and Radboud University Nijmegen and Katholieke Universiteit Leuven
Subjects: Scheme (programming language), FOS: Computer and information sciences, Computer science, Speech recognition, 02 engineering and technology, pronunciation modelling, Pronunciation, computer.software_genre, 01 natural sciences, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Phone, MLLR and MAP non-native accent adaptation, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, non-native speech recognition, Hidden Markov model, Adaptation (computer science), 010301 acoustics, Accent (sociolinguistics), computer.programming_language, phonetic confusion, Computer Science - Computation and Language, business.industry, 020206 networking & telecommunications, Artificial intelligence, business, Joint (audio engineering), computer, Computation and Language (cs.CL), Natural language processing, Word (computer architecture), model re-estimation
Abstract: International audience; In this paper, we present several adaptation methods for non-native speech recognition. We have tested pronunciation modelling, MLLR and MAP non-native pronunciation adaptation and HMM models retraining on the HIWIRE foreign accented English speech database. The ``phonetic confusion'' scheme we have developed consists in associating to each spoken phone several sequences of confused phones. In our experiments, we have used different combinations of acoustic models representing the canonical and the foreign pronunciations: spoken and native models, models adapted to the non-native accent with MAP and MLLR. The joint use of pronunciation modelling and acoustic adaptation led to further improvements in recognition accuracy. The best combination of the above mentioned techniques resulted in a relative word error reduction ranging from 46% to 71%.
Published: 2007
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42. 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

43. 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

44. An expert system for the automatic reading of French spectrograms

Author: Jean-Paul Haton, Noëlle Carbonell, Dominique Fohr, F. Lonchamp, and Jean-Marie Pierrel
Subjects: Computer science, business.industry, Speech recognition, media_common.quotation_subject, Legal expert system, computer.software_genre, Knowledge acquisition, Sketch, Expert system, Reading (process), Spectrogram, Artificial intelligence, Inference engine, business, computer, Natural language, Natural language processing, media_common
Abstract: We report on a long term project on the phonetic decoding of continuous speech, within the framework of an expert system. Spectrogram reading is the field of expertise under study, although it is clear that an automatic phonetic decoder should not be in the form of an expert system. We expect major improvements in the recognition rate when expert's rules will be incorporated into existing systems. We describe here both the method used to retrieve the human expertise and results by an experienced spectrogram reader on 50 phonetically-balanced sentences. We also sketch various ways in which the expert rules can be validated through an "inference engine" and actually used in a system. Finally, we discuss the impact this approach has on the design.
Published: 2005
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45. Interaction between stochastic modeling and knowledge-based techniques in acoustic-phonetic decoding of speech

Author: Noëlle Carbonell, Jean-Paul Haton, Dominique Fohr, Jean-François Mari, and A. Kriouille
Subjects: Computer science, Stochastic process, business.industry, media_common.quotation_subject, Speech recognition, computer.software_genre, Field (computer science), Quality (business), Artificial intelligence, Hidden Markov model, business, computer, Decoding methods, Natural language processing, media_common
Abstract: We present in this paper a new approach to acoustic-phonetic decoding of continuous speech that consists of integrating the two most promising present techniques in the field, i.e. stochastic modeling and knowledge-based expert systems. Our group has been developing several systems based on these two techniques during the past 15 years or so. Our present goal is to mix both in order to improve the overall quality of automatic phonetic decoding. This paper is concerned with the first developments of the project. We first present the main characteristics of the components of the system and its general architecture. We also present and discuss preliminary results concerning the segmentation of the speech wave into phonetic units and the gross labeling of these segments.
Published: 2005
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46. 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

47. 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

48. The automatic news transcription system: ANTS, some real time experiments

Author: Odile Mella, Dominique Fohr, Irina Illina, Christophe Cerisara, 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: Vocabulary, système de transcription de nouvelles émissions, Computer science, business.industry, Speech recognition, media_common.quotation_subject, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 05 social sciences, Word error rate, Machine learning, computer.software_genre, 050105 experimental psychology, broadcast news transcription system, 030507 speech-language pathology & audiology, 03 medical and health sciences, 0501 psychology and cognitive sciences, Segmentation, Artificial intelligence, Transcription (software), 0305 other medical science, business, computer, Radio broadcasting, media_common
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; This paper presents the recent development of ANTS, the Automatic News Transcription System of LORIA. This system was designed in the framework of ESTER, the French broadcast radio news transcription task evaluation. After describing its different components and some segmentation and recognition results on the ESTER database, we present a number of experiments focusing on the real-time version of ANTS. We evaluate the system with different number of Gaussians, sizes of vocabulary and decoder settings. The non real time version of ANTS yields an overall error rate of 36 % that can be compared to 44.5 % for the real time version.
Published: 2004
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49. Improving the performance of a keyword spotting system by using support vector machines

Author: Yassine Benayed, Dominique Fohr, Jean-Paul Haton, Gérard Chollet, 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), Laboratoire du traitement du signal et instrumentation (TSI), Université Jean Monnet [Saint-Étienne] (UJM)-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), 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: Computer science, Speech recognition, Feature vector, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Feature extraction, Measurement problem, 02 engineering and technology, Machine learning, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, modèle de markov caché, Phone, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov model, hidden markov model, business.industry, Pattern recognition, support vector machines svm, Speech processing, confidence measure, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Sound, Keyword spotting, 020201 artificial intelligence & image processing, Artificial intelligence, 0305 other medical science, business, computer, mesure de confiance
Abstract: Colloque avec actes et comité de lecture. internationale.; International audience; Support Vector Machines (SVM) represent a new approach to pattern classification developed from the theory of structural risk minimisation. In this paper, we propose an investigation into the application of SVM to the confidence measurement problem in speech recognition. Confidence measures are computed using the phone level information provided by a Hidden Markov Model (HMM) based speech recognizer. We use three kinds of average techniques as arithmetic, geometric and harmonic averages in order to compute a confidence measure for each word. The acceptance/rejection decision for a given word is based on the confidence feature vector which is processed by a SVM classifier. The performance of the proposed SVM classifier is compared with methods based on the averaging of phone confidence measures.
Published: 2004
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50. 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

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