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151. 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

152. 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

160. 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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161. Improved Neural Bag-of-Words Model to Retrieve Out-of-Vocabulary Words in Speech Recognition

Author

Irina Illina, Imran Sheikh, Georges Linarès, Dominique Fohr, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), 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), 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
Computer science, Speech recognition, Process (computing), oov, Context (language use), 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Task (project management), Bag-of-words model, 0202 electrical engineering, electronic engineering, information engineering, lvcsr, Embedding, Proper noun, 020201 artificial intelligence & image processing, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Layer (object-oriented design), proper names, 0105 earth and related environmental sciences
Abstract

International audience; Many Proper Names (PNs) are Out-Of-Vocabulary (OOV) words for speech recognition systems used to process di-achronic audio data. To enable recovery of the PNs missed by the system, relevant OOV PNs can be retrieved by exploiting the semantic context of the spoken content. In this paper, we explore the Neural Bag-of-Words (NBOW) model, proposed previously for text classification, to retrieve relevant OOV PNs. We propose a Neural Bag-of-Weighted-Words (NBOW2) model in which the input embedding layer is augmented with a context anchor layer. This layer learns to assign importance to input words and has the ability to capture (task specific) keywords in a NBOW model. With experiments on French broadcast news videos we show that the NBOW and NBOW2 models outper-form earlier methods based on raw embeddings from LDA and Skip-gram. Combining NBOW with NBOW2 gives faster convergence during training.

Published
2016
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162. 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

163. 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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164. 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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165. 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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167. 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

168. 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

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

Author

Odile Mella, Anne Bonneau, Dominique Fohr, Camille Fauth, Vincent Colotte, Yves Laprie, Jürgen Trouvain, Denis Jouvet, Analysis, perception and recognition of speech (PAROLE), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Allgemeine Linguistik Computational Linguistics and phonetics (Allgemeine Linguistik), Saarland University [Saarbrücken], ANR IFCASL, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
French, phonétique, phonetics, allemand, parole non-native, français, 02 engineering and technology, German, [SCCO.LING]Cognitive science/Linguistics, language learning, corpus de parole, lcsh:Social Sciences, lcsh:H, apprentissage des langues, 030507 speech-language pathology & audiology, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], 0305 other medical science, non native speech corpora
Abstract

La plupart des corpus en langue se focalisent sur les phénomènes linguistiques écrits et concernent l’anglais (voir le site web : « Learner corpora around the world » de l’Université de Louvain - Belgique). La recherche phonétique sur l’acquisition d’une L2 est généralement orientée vers l’étude des phénomènes segmentaux et la plupart des études ont également l’anglais comme langue cible. Les modèles de parole en L2 actuels - voir par exemple Speech Learning Model (Flege, 1995) ou Best’s Perceptual Assimilation Model (Best, 1995) – négligent bien souvent les aspects prosodiques. Notre étude concerne le français en tant que langue seconde et s’inscrit dans un projet plus vaste mené en partenariat avec une université allemande, dont l’un des buts est le développement de l’apprentissage des langues par ordinateur. (Projet ANR-DFG – Agence Nationale de la Recherche et Deutsche Forschungsgemeinschaft attribué à l’équipe Parole du LORIA UMR 7503, Nancy – France et à l’Equipe de Linguistique Computationnelle et de Phonétique FR 4.7 de l’Université de la Sarre Sarrebruck – Allemagne) dans lequel le français et l’allemand sont des langues cibles. Pour la paire allemand-français, peu de corpus parallèles sont disponibles. Nous présentons ici l’élaboration d’un corpus de productions orales de locuteurs natifs et non natifs pour la paire allemand-français. Notre corpus entend mettre au jour les déviations phonétiques et phonologiques que les locuteurs allemands produisent lorsqu’ils apprennent le français. Ce travail s’insère dans un projet plus global, Ce projet entend étudier les difficultés que les locuteurs français rencontrent lorsqu’ils apprennent l’allemand, et réciproquement. Aussi, cinquante locuteurs allemands seront recrutés dans des milieux universitaires et scolaires (niveau lycée) en Allemagne et cinquante locuteurs français dans les mêmes milieux en France. Il s’agit pour les deux populations de produire d’une part le corpus en langue étrangère (en langue française pour les locuteurs allemands et en langue allemande pour les locuteurs français) mais également le corpus en langue maternelle (en allemand pour les allemands et en français pour les français). Les corpus ainsi obtenus devraient nous permettre d’identifier les difficultés que les locuteurs allemands ou français rencontrent lorsqu’ils apprennent le français ou l’allemand. Les données de contrôle sont doubles puisque l’on pourra à la fois se référer aux productions des apprenants dans leur langue maternelle (ici l’allemand), mais également à celles de locuteurs natifs (ici germanophones). Nous ne présenterons ici que la constitution du corpus en français.

Published
2014
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170. 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

171. 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

172. 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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173. 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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174. 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

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

Author

Odile Mella, Jean-Paul Haton, Joseph Razik, Dominique Fohr, Université de Toulon (UTLN), Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)

Subjects
likelihood ratio, Modalities, Computer science, Low Confidence, Speech recognition, Posterior probability, Frame (networking), speech recognition, 020206 networking & telecommunications, 02 engineering and technology, frame-synchronous measure, Measure (mathematics), posterior probability, Comprehension, Artificial Intelligence, Confidence measure, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Transcription (software), Software
Abstract

International audience; In this paper, we introduce two new confidence measures for large vocabulary speech recognition systems. The major feature of these measures is that they can be computed without waiting for the end of the audio stream. We proposed two kinds of confidence measures: frame-synchronous and local. The frame-synchronous ones can be computed as soon as a frame is processed by the recognition engine and are based on a likelihood ratio. The local measures estimate a local posterior probability in the vicinity of the word to analyze. We evaluated our confidence measures within the framework of the automatic transcription of French broadcast news with the EER criterion. Our local measures achieved results very close to the best state-of-the-art measure (EER of 23% compared to 22.0%). We then conducted a preliminary experiment to assess the contribution of our confidence measure in improving the comprehension of an automatic transcription for the hearing impaired. We introduced several modalities to highlight words of low confidence in this transcription. We showed that these modalities used with our local confidence measure improved the comprehension of automatic transcription

Published
2011
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176. A wavelet-based parameterization for speech/music discrimination

Author

Emmanuel Didiot, Irina Illina, Odile Mella, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Frequency band, Computer science, Speech recognition, Word error rate, Speech/music discrimination, 02 engineering and technology, Wavelets, Theoretical Computer Science, Static parameters, Reduction (complexity), 030507 speech-language pathology & audiology, 03 medical and health sciences, Wavelet, Segmentation, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Signal processing, Human-Computer Interaction, Sound recording and reproduction, Computer Science::Sound, Dynamic parameters, 020201 artificial intelligence & image processing, Mel-frequency cepstrum, 0305 other medical science, Long-term parameters, Software, Energy (signal processing)
Abstract

International audience; This paper addresses the problem of parameterization for speech/music discrimination. The current successful parameterization based on cepstral coefficients uses the Fourier transformation (FT), which is well adapted for stationary signals. In order to take into account the non-stationarity of music/speech signals, this work proposes to study wavelet-based signal decomposition instead of FT. Three wavelet families and several numbers of vanishing moments have been evaluated. Different types of energy, calculated for each frequency band obtained from wavelet decomposition, are studied. Static, dynamic and long-term parameters were evaluated. The proposed parameterization are integrated into two class/non-class classifiers: one for speech/non-speech, one for music/non-music. Different experiments on realistic corpora, including different styles of speech and music (Broadcast News, Entertainment, Scheirer), illustrate the performance of the proposed parameterization, especially for music/non-music discrimination. Our parameterization yielded a significant reduction of the error rate. More than 30% relative improvement was obtained for the envisaged tasks compared to MFCC parameterization.

Published
2010
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177. 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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178. 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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179. 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

180. 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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181. 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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182. 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

183. 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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184. 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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185. 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

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

Author

Olivier Boëffard, Frédéric Bimbot, Delphine Charlet, Sacha Krstulovic, Dominique Fohr, Odile Mella, Speech and sound data modeling and processing (METISS), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Human-machine spoken dialogue (CORDIAL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-INRIA Rennes, Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT), France Télécom Recherche & Développement (FT R&D), France Télécom, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-INRIA Rennes

Subjects
Linguistics and Language, speaker selection, Computer science, speech and speaker recognition, media_common.quotation_subject, Speech recognition, Context (language use), Speech synthesis, 02 engineering and technology, computer.software_genre, Language and Linguistics, 030507 speech-language pathology & audiology, 03 medical and health sciences, Similarity (network science), cost minimization, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Cluster analysis, Selection (genetic algorithm), speech database, media_common, speaker clustering, multi-models, optimal coverage, Database, Communication, Variety (linguistics), Similitude, Computer Science Applications, Modeling and Simulation, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Data mining, 0305 other medical science, computer, Software
Abstract

International audience; In the context of the Neologos French speech database creation project, we have defined a general methodology for the selection of representative speaker recordings. The selection aims at insuring a good coverage in terms of speaker variability while limiting the number of recorded speakers. This makes the resulting database both more adapted to the development of recently proposed multi-model methods and cheaper to collect. The presented methodology proposes to operate a selection by optimizing a quality criterion defined in a variety of speaker similarity modeling frameworks. The selection can be operated and validated with respect to a unique similarity criterion, using classical clustering methods such as Hierarchical or K-Medians clustering, or it can be operated and validated across several speaker similarity criteria, thanks to a newly developed clustering method called Focal Speakers Selection. In this framework, four different speaker similarity criteria are tested, and three different speaker clustering algorithms are compared. Results pertaining to the collection of the Neologos database are also discussed.

Published
2006
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188. 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

189. 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

190. 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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191. 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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192. 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

193. 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

194. 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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195. 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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196. 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

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

Author

Dominique Fohr, Irina Illina, Filipp Korkmazsky, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Normalization (statistics), Computer science, Color normalization, Speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Normalization (image processing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Linear interpolation, 030507 speech-language pathology & audiology, 03 medical and health sciences, Histogram, 0202 electrical engineering, electronic engineering, information engineering, histogram equalization for speech recogntion, Histogram equalization, égalisation d'histogramme pour la reconnaissance de la parole, business.industry, Histogram matching, 020206 networking & telecommunications, Pattern recognition, Weighting, Adaptive histogram equalization, Artificial intelligence, 0305 other medical science, business, Interpolation
Abstract

Colloque avec actes et comité de lecture. internationale.; International audience; This paper presents a novel approach to speech data normalization by introducing interpolation for histogram equalization. We study different ways of histogram interpolation that inhence this normalization technique. We found that using a special weighting factor to combine current and past test sentence statistics improved speech recognition performance.

Published
2004

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

Author

Salma Jamoussi, Kamel Smaïli, Dominique Fohr, Jean-Paul Haton, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Loria, Publications

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], réseaux bayésiens, classification non supervisée, cutoclass, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], mesures sémantiques, compréhension de la parole, représentation vectorielle des mots, extraction automatique des concepts, autoclass
Abstract

Colloque avec actes et comité de lecture. nationale.; National audience; In this work, we present a complete speech understanding system based on our speech recognizer: ESPERE. The input signal is processed and the best sentence is then proposed to the understanding module. In our case, the understanding problem is considered as a matching process between two different languages. At the entry, the request expressed in natural language and at the output the corresponding SQL form. The SQL request is obtained after an intermediate step in which the entry is expressed in terms of concepts. A concept represents a given meaning, it is defined by a set of words sharing the same semantic properties. In this paper, we propose a new Bayesian classifier to automatically extract the underlined concepts. We also propose a new approach for vector representation of words. Then, we describe the postprocessing step during which, we label our sentences and we generate the corresponding SQL queries. We conclude our paper by describing the integration step of our understanding module in a complete platform of human-machine oral intercation.

Published
2004

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

Author

Christophe Cerisara, Odile Mella, Irina Illina, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, business.industry, Speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], French, 020206 networking & telecommunications, 02 engineering and technology, language.human_language, 030507 speech-language pathology & audiology, 03 medical and health sciences, Phone, reconnaissance de la parole de nouvelles émissions, broadcast news speech recognition, 0202 electrical engineering, electronic engineering, information engineering, language, Transcription (software), 0305 other medical science, business, Radio broadcasting
Abstract

Colloque avec actes et comité de lecture. internationale.; International audience; In the framework of ESTER, the recent French broadcast radio news transcription task evaluation, we have developed the first version of ANTS, the Automatic News Transcription System of LORIA. This paper describes the different components of the ANTS system and provides some first recognition results on the ESTER database. Then it presents several experiments carried out on this system to take into account the specificities of the French language: how accurate should the phones models be and how to deal with the problem of the liaisons between words.

Published
2004

200. A New Keyword Spotting Approach Based on Reward Function

Author

Jean-Paul Haton, Yassine Benayed, Gérard Chollet, Dominique Fohr, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), Laboratoire du traitement du signal et instrumentation (TSI), Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien [Saint Etienne] (LHC), and Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, media_common.quotation_subject, Speech recognition, modèles de markov cachés, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 02 engineering and technology, Markov model, Viterbi algorithm, 01 natural sciences, mots hors-vocabualire, symbols.namesake, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov model, 010301 acoustics, media_common, Grammar, business.industry, Maximum-entropy Markov model, hidden markov models, 020206 networking & telecommunications, Pattern recognition, Mixture model, Speech processing, filler model, modèle poubelle, out-of-vocabulary words, ComputingMethodologies_PATTERNRECOGNITION, Keyword spotting, symbols, Artificial intelligence, gmm, business
Abstract

Colloque avec actes et comité de lecture. nationale.; National audience; In this paper, we compare the performance achieved by different word-spotting techniques based on hidden Markov models. We propose two methods to detect keywords, the first one uses a GMM (Gaussian Mixture Model) as a filler model to absorb the out-of-vocabulary words. The second is an alternative approach which does not attempt to model out-of-vocabulary words, instead, it uses buckled phonemes basedgrammar. Furthermore, it uses different reward functions to favourite the recognition of the keywords phonemes.

Published
2003

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