Impact du degre de supervision sur l'adaptation a un domaine d'un modele de langage a partir du Web

Domain adaptation of a language model aims at re-estimating word sequence probabilities in order to better match the peculiarities of a given broad topic of interest. To achieve this task, a common strategy consists in retrieving adaptation texts from the Internet based on a given domain-representative seed text. In this paper, we study the influence of the choice of this seed text on the adaptation process and on the performances of adapted language models in automatic speech recognition. More precisely, the goal of this original study is to analyze the differences between supervised adaptation, in which the seed text is manually generated, and unsupervised adaptation, where the seed text is an automatic transcript. Experiments carried out on videos from a real-world use case mainly show that differences vary according to adaptation scenarios and that the unsupervised approach is globally convincing, especially according to its low cost

Language model adaptation for lecture transcription by document retrieval

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_14With the spread of MOOCs and video lecture repositories it is more important than ever to have accurate methods for automatically transcribing video lectures. In this work, we propose a simple yet effective language model adaptation technique based on document retrieval from the web. This technique is combined with slide adaptation, and compared against a strong baseline language model and a stronger slide-adapted baseline. These adaptation techniques are compared within two different acoustic models: a standard HMM model and the CD-DNN-HMM model. The proposed method obtains improvements on WER of up to 14% relative with respect to a competitive baseline as well as outperforming slide adaptation.The research leading to these results has received fund-ing from the European Union Seventh Framework Programme (FP7/2007-2013)under grant agreement no 287755 (transLectures) and ICT Policy Support Pro-gramme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no 621030 (EMMA), the Spanish MINECO Active2Trans (TIN2012-31723) research project and the Spanish Government with the FPU scholarships FPU13/06241 and AP2010-4349.Martínez-Villaronga, A.; Del Agua Teba, MA.; Silvestre Cerdà, JA.; Andrés Ferrer, J.; Juan, A. (2014). Language model adaptation for lecture transcription by document retrieval. En Advances in Speech and Language Technologies for Iberian Languages. Springer Verlag (Germany). 129-137. https://doi.org/10.1007/978-3-319-13623-3_14S129137coursera.org: Take the World’s Best Courses, Online, For Free, http://www.coursera.org/poliMedia: Videolectures from the “Universitat Politècnica de València, http://polimedia.upv.es/catalogo/SuperLectures: We take full care of your event video recordings, http://www.superlectures.comtransLectures, https://translectures.eu/transLectures-UPV Toolkit (TLK) for Automatic Speech Recognition, http://translectures.eu/tlkUdacity: Learn, Think, Do, http://www.udacity.com/Videolectures.NET: Exchange Ideas and Share Knowledge, http://www.videolectures.net/del-Agua, M.A., Giménez, A., Serrano, N., Andrés-Ferrer, J., Civera, J., Sanchis, A., Juan, A.: The translectures-UPV toolkit. In: Navarro Mesa, J.L., Giménez, A.O., Teixeira, A. (eds.) IberSPEECH 2014. LNCS (LNAI), vol. 8854, pp. 269–278. Springer, Heidelberg (2014)Chang, P.C., Shan Lee, L.: Improved language model adaptation using existing and derived external resources. In: Proc. of ASRU, pp. 531–536 (2003)Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. Computer Speech & Language 13(4), 359–393 (1999)Jelinek, F., Mercer, R.L.: Interpolated Estimation of Markov Source Parameters from Sparse Data. In: Proc. of the Workshop on Pattern Recognition in Practice, pp. 381–397 (1980)Ketterl, M., Schulte, O.A., Hochman, A.: Opencast matterhorn: A community-driven open source solution for creation, management and distribution of audio and video in academia. In: Proc. of ISM, pp. 687–692 (2009)Kneser, R., Ney, H.: Improved Backing-off for M-gram Language Modeling. In: Proc. of ICASSP, pp. 181–184 (1995)Lecorv, G., Gravier, G., Sbillot, P.: An unsupervised web-based topic language model adaptation method. In: Proc. of ICASSP 2008, pp. 5081–5084 (2008)Martínez-Villaronga, A., del Agua, M.A., Andrés-Ferrer, J., Juan, A.: Language model adaptation for video lectures transcription. In: Proc. of ICASSP, pp. 8450–8454 (2013)Munteanu, C., Penn, G., Baecker, R.: Web-based language modelling for automatic lecture transcription. In: Proc. of INTERSPEECH, pp. 2353–2356 (2007)Rogina, I., Schaaf, T.: Lecture and presentation tracking in an intelligent meeting room. In: Proc of ICMI, pp. 47–52 (2002)Schlippe, T., Gren, L., Vu, N.T., Schultz, T.: Unsupervised language model adaptation for automatic speech recognition of broadcast news using web 2.0, pp. 2698–2702 (2013)Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent deep neural networks for conversational speech transcription. In: Proc. of ASRU, pp. 24–29 (2011)Silvestre, J.A., et al.: Translectures. In: Proc. of IberSPEECH 2012, pp. 345–351 (2012)Smith, R.: An overview of the tesseract ocr engine. In: Proc. of ICDAR 2007, pp. 629–633 (2007)Stolcke, A.: SRILM – an extensible language modeling toolkit. In: Proc. of ICSLP, pp. 901–904 (2002)Tsiartas, A., Georgiou, P., Narayanan, S.: Language model adaptation using www documents obtained by utterance-based queries. In: Proc. of ICASSP, pp. 5406–5409 (2010

An unsupervised web-based topic language model adaptation method

This paper focuses on a solution to better adapt ASR systems, whose language models (LM) are usually trained on topic-independent corpora, to new topics, in particular in the case of broadcast news. We propose a new complete and fully unsupervised technique that selects keywords from each segment using information retrieval methods, to build a thematically coherent adaptation corpus from the Internet. The LM used for the initial transcription is then adapted before rescoring word lattices. Experimental results demonstrate the validity of the proposed adaptation technique with a significant reduction of the perplexity after LM adaptation. Word error rates are also improved in some cases though to a lesser extent. Index Terms — Speech recognition, natural languages, Internet 1

Modèles de langage ad hoc pour la reconnaissance automatique de la parole

Les trois piliers d un système de reconnaissance automatique de la parole sont le lexique,le modèle de langage et le modèle acoustique. Le lexique fournit l ensemble des mots qu il est possible de transcrire, associés à leur prononciation. Le modèle acoustique donne une indication sur la manière dont sont réalisés les unités acoustiques et le modèle de langage apporte la connaissance de la manière dont les mots s enchaînent.Dans les systèmes de reconnaissance automatique de la parole markoviens, les modèles acoustiques et linguistiques sont de nature statistique. Leur estimation nécessite de gros volumes de données sélectionnées, normalisées et annotées.A l heure actuelle, les données disponibles sur le Web constituent de loin le plus gros corpus textuel disponible pour les langues française et anglaise. Ces données peuvent potentiellement servir à la construction du lexique et à l estimation et l adaptation du modèle de langage. Le travail présenté ici consiste à proposer de nouvelles approches permettant de tirer parti de cette ressource.Ce document est organisé en deux parties. La première traite de l utilisation des données présentes sur le Web pour mettre à jour dynamiquement le lexique du moteur de reconnaissance automatique de la parole. L approche proposée consiste à augmenter dynamiquement et localement le lexique du moteur de reconnaissance automatique de la parole lorsque des mots inconnus apparaissent dans le flux de parole. Les nouveaux mots sont extraits du Web grâce à la formulation automatique de requêtes soumises à un moteur de recherche. La phonétisation de ces mots est obtenue grâce à un phonétiseur automatique.La seconde partie présente une nouvelle manière de considérer l information que représente le Web et des éléments de la théorie des possibilités sont utilisés pour la modéliser. Un modèle de langage possibiliste est alors proposé. Il fournit une estimation de la possibilité d une séquence de mots à partir de connaissances relatives à existence de séquences de mots sur le Web. Un modèle probabiliste Web reposant sur le compte de documents fourni par un moteur de recherche Web est également présenté. Plusieurs approches permettant de combiner ces modèles avec des modèles probabilistes classiques estimés sur corpus sont proposées. Les résultats montrent que combiner les modèles probabilistes et possibilistes donne de meilleurs résultats que es modèles probabilistes classiques. De plus, les modèles estimés à partir des données Web donnent de meilleurs résultats que ceux estimés sur corpus.The three pillars of an automatic speech recognition system are the lexicon, the languagemodel and the acoustic model. The lexicon provides all the words that can betranscribed, associated with their pronunciation. The acoustic model provides an indicationof how the phone units are pronounced, and the language model brings theknowledge of how words are linked. In modern automatic speech recognition systems,the acoustic and language models are statistical. Their estimation requires large volumesof data selected, standardized and annotated.At present, the Web is by far the largest textual corpus available for English andFrench languages. The data it holds can potentially be used to build the vocabularyand the estimation and adaptation of language model. The work presented here is topropose new approaches to take advantage of this resource in the context of languagemodeling.The document is organized into two parts. The first deals with the use of the Webdata to dynamically update the lexicon of the automatic speech recognition system.The proposed approach consists on increasing dynamically and locally the lexicon onlywhen unknown words appear in the speech. New words are extracted from the Webthrough the formulation of queries submitted toWeb search engines. The phonetizationof the words is obtained by an automatic grapheme-to-phoneme transcriber.The second part of the document presents a new way of handling the informationcontained on the Web by relying on possibility theory concepts. A Web-based possibilisticlanguage model is proposed. It provides an estition of the possibility of a wordsequence from knowledge of the existence of its sub-sequences on the Web. A probabilisticWeb-based language model is also proposed. It relies on Web document countsto estimate n-gram probabilities. Several approaches for combining these models withclassical models are proposed. The results show that combining probabilistic and possibilisticmodels gives better results than classical probabilistic models alone. In addition,the models estimated from Web data perform better than those estimated on corpus.AVIGNON-Bib. numérique (840079901) / SudocSudocFranceF

Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists