S. Huet, P. Sébillot, and &. , Gravier Category: Verb Features: {verbtype=past, voice=active, inverted=yes, gapp=yes, mood=whquestion

L. Role=need3, Pas de modifieur ) Dependent Positional Constraints

[. Antoine and J. Goulian, « Word Order Variations and Spoken Man- Machine Dialogue in French: a Corpus Analysis on the ATIS Domain, Proc. of Corpus Linguistics, 2001.

J. [. Allauzen and . Gauvain, « Adaptation automatique du modèle de langage d'un système de transcription de journaux parlés, Traitement Automatique des Langues (TAL), vol.44, issue.1, pp.11-31, 2003.

J. [. Allauzen and . Gauvain, Open Vocabulary ASR for Audiovisual Document Indexation, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005.
DOI : 10.1109/ICASSP.2005.1415288

]. J. All94 and . Allen, « How do Humans Process and Recognize Speech?, IEEE Transactions on Speech and Audio Processing, pp.567-577, 1994.

. [. Blanche-benveniste, Le français parlé : ´ etudes grammaticales, ´ Editions du CNRS, 1990.

. [. Blanche-benveniste, Approches de la langue parlée en français. Gap -Paris : Ophrys, 1997.

C. Benzitoun, E. Campione, J. Deulofeu, S. Henry, F. Sabio et al., « L'analyse syntaxique de l'oral : probì emes et méthode ». Dans Actes de la journée d'´ etude de l'ATALA sur l'annotation syntaxique de corpus, 2004.

S. [. Boufaden, B. Delisle, and . Moulin, « Analyse syntaxique robuste de dialogue retranscrits : peut-on vraiment traiter l'oraì a partir de l'´ ecrit ?

M. [. Boula-de-mareüil, V. Adda-decker, and . Gendner, « Liaisons in French: a Corpus-Based Study Using Morpho-Syntactic Information, Proc. of the 15th International Congress of Phonetic Sciences, 2003.

R. [. Bigi, T. De-mori, and . Spriet, « Reconnaissance thématiquè a partir de textes dictés et adaptation dynamique de modèles de langage thématiques, Actes des 23èmes Journées d' ´ Etudes sur la Parole (JEP), 2000.

]. J. Bel98 and . Bellegarda, « A Multispan Language Modeling Framework for Large Vocabulary Speech Recognition, IEEE Transactions on Speech and Audio Processing, vol.6, issue.5, pp.456-467, 1998.

]. J. Bel00 and . Bellegarda, « Large Vocabulary Speech Recognition with Multispan Statistical Language Models, IEEE Transactions on Speech and Audio Processing, pp.76-84, 2000.

]. C. Ben04 and . Benzitoun, « L'annotation syntaxique de corpus oraux constitue-t-elle unprobì eme spécifique ?, Actes de la 8` eme Rencontre desÉtudiantsdes´desÉtudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL), F` es, 2004.

R. [. Brill, J. C. Florian, L. Henderson, and . Mangu, « Beyond N- Grams: Can Linguistic Sophistication Improve Language Modeling?, Proc. of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics (COLING-ACL), 1998.

E. [. Barras, Z. Geoffrois, M. Wu, and . Liberman, Transcriber: Development and use of a tool for assisting speech corpora production, Speech Communication, vol.33, issue.1-2, pp.5-22, 2001.
DOI : 10.1016/S0167-6393(00)00067-4

Y. [. Bigi, R. D. Huang, and . Mori, « Vocabulary and Language Model Adaptation Using Information Retrieval, Proc. of the 8th International Conference on Spoken Language Processing (ICSLP), 2004.

S. Huet, P. Sébillot, &. Béchet, A. Nasr, T. Spriet et al., Just-in-Time Language Modelling « Recognition Performance of a Large-Scale Dependency-Grammar Language Model « Integrating Statistical and Rule-Based Knowledge for Continuous German Speech Recognition, Varigram Histories Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing Proc Modèles de langagè a portée variable : application au traitement des homophones ». Dans Actes de la 6` eme conférence sur le Traitement Automatique des Langues Naturelles (TALN) Proc. of the 5th International Conference on Spoken Language Processing Proc. of the 8th European Conference on Speech Communication and Technology (Eurospeech) Variable-Length Sequence Modeling: Multigrams ». Signal Processing Letters, IEEE Proc. of the Sixth Applied NLP Thèse de doctoratBV05] C. Benzitoun et J. Véronis. «Probì emes d'annotation d'un corpus oral dans le cadre de la campagne EASY ». Dans Actes de la 12ème conférence sur le Traitement Automatique des Langues Naturelles (TALN)Cam01] E. Campione. « ´ Etiquetage prosodique semi-automatique de corpus oraux : algorithmes et méthodologie Thèse de doctoratCG98] S. F. Chen et J. Goodman. « An Empirical Study of Smoothing Techniques for Language Modeling ». Rapport technique, pp.111-113, 1995.

]. L. Irisa-[-cgl-+-01, J. Chen, L. Gauvain, G. Lamel, and . Adda, Adda-Decker. « Using Information Retrieval Methods for Language Model Adaptation, Proc. of the 7th European Conference on Speech Communication and Technology (Eurospeech), 2001.

J. [. Chen, L. Gauvain, G. Lamel, and . Adda, « Unsupervised Language Model Adaptation for Broadcast News, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003.

J. [. Chen, L. Gauvain, G. Lamel, and . Adda, « Dynamic Language Modeling for Broadcast News, Proc. of the 8th International Conference on Spoken Language Processing (ICSLP), ? ?le de Jeju, Corée du Sud, 2004.

]. E. Cha00 and . Charniak, « A Maximum-Entropy-Inspired Parser, Proc. of the 1st Conference of the North American Chapter, 2000.

]. E. Cha01 and . Charniak, « Immediate-Head Parsing for Language Models, Proc. of the 39th Annual Meeting of the Association for Computational Linguistics (ACL), 2001.

F. [. Chelba and . Jelinek, Structured language modeling, Computer Speech & Language, vol.14, issue.4, pp.283-332, 2000.
DOI : 10.1006/csla.2000.0147

[. Chow and S. Roukos, Speech understanding using a unification grammar, International Conference on Acoustics, Speech, and Signal Processing, 1989.
DOI : 10.1109/ICASSP.1989.266530

A. [. Clarkson and . Robinson, Language model adaptation using mixtures and an exponentially decaying cache, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997.
DOI : 10.1109/ICASSP.1997.596049

T. [. Clarkson and . Robinson, « Towards Improved Language Model Evaluation Measures, Proc. of the 6th European Conference on Speech Communication and Technology (Eurospeech), 1999.
DOI : 10.1006/csla.2000.0156

[. Chappelier, M. Rajman, R. Aragüés, and A. Rozenknop, « Lattice Parsing for Speech Recognition, Actes de la 6` eme conférence sur le Traitement Automatique des Langues Naturelles (TALN), 1999.

[. Chow and R. Schwartz, The N-Best algorithm, Proceedings of the workshop on Speech and Natural Language , HLT '89, 1989.
DOI : 10.3115/1075434.1075467

P. , S. Huet, P. Sébillot, &. G. Gravier, J. Campione et al., Campione et J. Véronis. « Pauses et hésitations en français spontané Integrated Reference Corpora for Spoken Romance Languages », Chapitre 3. The French corpus Amsterdam: John Benjamins « Large-Scale Lexical Semantics for Speech Recognition Support « Language Modeling by Variable Length Sequences: Theoretical Formulation and Evaluation of Multigrams « Learning a Syntagmatic and Paradigmatic Structure from Language Data with a Bi-Multigram Model, Etude des relations entre pauses et ponctuations pour la synthèse de la parolè a partir de texte Proc. of the 5th European Conference on Speech, Communication, Technology (Eurospeech) Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Gemini: A Natural Language System for Spoken Language Understanding Proc. of the 31st Annual Meeting of the Association for Computational Linguistics (ACL)DGP99] N. Deshmukh, A. Ganapathiraju et J. Picone. « Hierarchical Search for Large Vocabulary Conversational Speech Recognition Proc. of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics (COLING-ACL) Derouault. « A Morphological Model for Large Vocabulary Speech Recognition Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing Bayesian Estimation Methods for N-Gram Language Model Adaptation Proc. of the 4th International Conference on Spoken Language Processing (ICSLP)Fed99] M. Federico. « Efficient Language Model Adaptation through MDI Estimation Proc. of the 6th European Conference on Speech, Communication , Technology (Eurospeech), pp.111-13384, 1990.

. [. Irisa, J. Farhat, D. Isabelle, M. O-'shaughnessy, B. Franz et al., « Clustering Words for Statistical Language Models Based on Contextual Word Similarity, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Information Access in Large Spoken Archives Proc. of the ISCA Multilingual Spoken Document Retrieval Workshop Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation Proc. of 37th Annual Meeting of the Association for Computational Linguistics (ACL), 1996.

[. Gauvain, G. Adda, M. Adda-decker, A. Allauzen, V. Gendner et al., « Where are we in Transcribing French Broadcast News? Adda-Decker. « Analyse comparative de corpus oraux etécritsetécrits français : mots, lemmes et classes morpho-syntaxiques, Proc. of the 9th European Conference on Speech Communication and Technology (Eurospeech) Transcription de la parole conversationnelle Actes des 25èmes Journées d' ´ Etudes sur la Parole (JEP), F` es, MarocGar95] R. Garside. « Spoken English on Computer: Transcription, Mark-up and Application », Chapitre Grammatical Tagging of the Spoken Part of the British National Corpus: A Progress ReportGeu96] P. Geutner. « Introducing Linguistic Constraints into Statistical Language Modeling Proc. of the 4th International Conference on Spoken Language Processing (ICSLP)GH99] D. Gildea et T. Hofmann. « Topic-Based Language Models Using EM, pp.161-167, 1995.

M. [. Gao, K. Li, and . Lee, « N-Gram Distribution Based Language Model Adaptation, Dans Proc. of the 6th European Conference on Speech Communication and Technology (Eurospeech) Proc. of the 6th International Conference on Spoken Language ProcessingGoo01] J. T. Goodman. « A Bit of Progress in Language Modeling, Extended Version ». Rapport technique, Microsoft ResearchGSTN96] F. Gallwitz, E. G. Schukat-Talamazzini et H. Niemann. « Integrating Large Context Language Models into a Real Time Word Recognizer Proc. of the 3rd Slovenian-German and the 2nd SDRV Workshop, 1996.

P. , S. Huet, P. Sébillot, and &. Tamine, Pour une grammaire de l'´ ecrit Guénot. « Parsing de l'oral : traiter les disfluences « A Language Model Combining Trigrams and Stochastic Context-Free Grammars, Proc. of the 5th International Conference on Spoken Language ProcessingHee99] P. A. Heeman. « POS Tags and Decision Trees for Language Modeling, 1998.

]. S. Hen02b and . Henry, Quelles répétitionsrépétitions`répétitionsà l'oral ? Esquisse d'une typologie « ´ Etude des répétitions en français parlé spontané pour les technologies de la parole, Dans Proc. of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora Actes des 2` emes Journées de Linguistique de Corpus Actes de la 6` eme Rencontre desÉtudiantsdes´desÉtudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL) Harper et R. A. Helzerman. « Extensions to Constraint Dependency Parsing for Spoken Language Processing ». Computer Speech and Language Hall et M. Johnson. « Attention Shifting for Parsing Speech Proc. of the 42nd Meeting of the Association for Computational Linguistics (ACL)HJJ + 99] M. P. Harper, M. T. Johnson, L. H. Jamieson, S. A. Hockema et C. M, pp.187-234, 1995.

. White, . Parser, M. P. Word-recognizer-using-word-graphs, L. H. Harper, C. D. Jamieson et al., « Integrating Language Models with Speech Recognition Fragments and Repeats in Spontaneous Spoken French, Proc Proc. of the AAAI94 Workshop on the Integration of Natural Language and Speech Processing Proceedings of Disfluency in Spontaneous Speech Workshop (DISS)HW94] A. Hauenstein et H. Weber. « An Investigation of Tightly-Coupled Time- Synchronous Speech Language Understanding Using a Unification Grammar Proc. of the 12th National Conference on Artificial Intelligence Irisa Workshop on the Integration of Natural Language and Speech Processing, 1994.

S. [. Isotani and . Matsunaga, Speech recognition using a stochastic language model integrating local and global constraints, Proceedings of the workshop on Human Language Technology , HLT '94, 1994.
DOI : 10.3115/1075812.1075829

M. [. Iyer and . Ostendorf, Modeling long distance dependence in language: topic mixtures versus dynamic cache models, IEEE Transactions on Speech and Audio Processing, vol.7, issue.1, pp.30-39, 1999.
DOI : 10.1109/89.736328

M. Johnson and E. Charniak, A TAG-based noisy channel model of speech repairs, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics , ACL '04, 2004.
DOI : 10.3115/1218955.1218960

]. F. Jel97 and . Jelinek, Statistical Methods for Speech Recognition, 1997.

J. [. Jelinek and . Lafferty, « Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Grammars ». Computation Linguistics, pp.315-323, 1991.

C. D. Jurafsky, J. Wooters, A. Segal, E. Stolcke, G. Fosler et al., Using a stochastic context-free grammar as a language model for speech recognition, 1995 International Conference on Acoustics, Speech, and Signal Processing, 1995.
DOI : 10.1109/ICASSP.1995.479396

]. S. Kat87 and . Katz, « Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recognizer, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.35, issue.3, pp.400-401, 1987.

R. [. Kuhn and . Mori, A cache-based natural language model for speech recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.6, pp.570-583, 1990.
DOI : 10.1109/34.56193

T. [. Kita, H. Kawabata, and . Saito, HMM continuous speech recognition using predictive LR parsing, International Conference on Acoustics, Speech, and Signal Processing, 1989.
DOI : 10.1109/ICASSP.1989.266524

S. Huet, P. Sébillot, &. G. Gravier, H. Kneser, and . Ney, « Improved Clustering Techniques for Class-Based Statistical Language Modelling, Proc. of the 3rd European Conference on Speech Communication and Technology (Eurospeech), 1993.

]. R. Kne96, . [. Kneser, H. Kuhn, E. G. Niemann, and . Schukat-talamazzini, « Statistical Language Modeling Using a Variable Context Length, Proc. of the 4th International Conference on Spoken Language Processing (ICSLP) Ergodic Hidden Markov Models and Polygrams for Language Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1994.

[. J. Kuo and W. , « Phrase-Based Language Models for Speech Recognition, Proc. of the 6th European Conference on Speech Comunication and Technology (Eurospeech), 1999.

V. [. Kneser and . Steinbiss, On the dynamic adaptation of stochastic language models, IEEE International Conference on Acoustics Speech and Signal Processing, 1993.
DOI : 10.1109/ICASSP.1993.319375

A. [. Kemp and . Waibel, « Reducing the OOV Rate in Broadcast News Speech Recognition, Proc. of the 5th International Conference on Spoken Language Processing (ICSLP), 1998.

J. [. Khudanpur, D. Wu, J. Linares, J. Benedí, and . Sánchez, « A Maximum Entropy Language Model to Integrate N-Grams and Topic Dependencies for Conversational Speech Recognition « A Hybrid Language Model Based on a Combination of N-Grams and Stochastic Context-Free Grammars, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing ACM Transactions on Asian Language Information Processing (TALIP), pp.113-127, 1999.

A. [. Leech, M. Mcenery, and . Wynne, « Corpus Annotation », Chapitre Further Levels of Annotation, pp.85-101, 1997.

I. Liu, A. Stolcke, M. P. Harper, and E. Shriberg, « Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech, Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2004.

D. [. Lafferty, D. Sleator, and . Temperley, « Grammatical Trigrams: A Probabilistic Link Grammar, Proc. of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, 1992.

R. [. Mendes and M. F. Amaro, Bacelar do Nascimento. « Reusing Available Resources for Tagging a Spoken Portuguese Corpus, Proc. of the Workshop on Tagging and Shallow Processing of Portuguese (TASHA), 2003.

E. [. Mangu, A. Brill, and . Stolcke, Finding consensus in speech recognition: word error minimization and other applications of confusion networks, Computer Speech & Language, vol.14, issue.4, pp.373-400, 2000.
DOI : 10.1006/csla.2000.0152

]. L. Mel00 and . Melis, « Le français parlé et le françaisfrançaisécrit, une oppositionàopposition`oppositionà géométrie variable, Romaneske, vol.25, issue.3, pp.56-66, 2000.

J. [. Moreno and . Guirao, « Tagging a Spontaneous Speech Corpus of Spanish, Proc. of Recent Advances in Natural Language Processing (RANLP), 2003.

M. Meteer and R. Iyer, « Modeling Conversational Speech for Speech Recognition, Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 1996.

J. [. Martin, H. Liermann, and . Ney, « Adaptive Topic Dependent Language Modelling Using Word-Based Varigrams, Proc. of the 5th European Conference on Speech Communication and Technology (Eurospeech), 1997.

F. [. Maltese and . Mancini, An automatic technique to include grammatical and morphological information in a trigram-based statistical language model, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992.
DOI : 10.1109/ICASSP.1992.225948

]. R. Moo99 and . Moore, « Computational Models of Speech Pattern Processing », Chapitre Using Natural-Language Knowledge Sources in Speech Recognition, pp.304-327, 1999.

F. [. Moore, H. Pereira, and . Murveit, Integrating speech and natural-language processing, Proceedings of the workshop on Speech and Natural Language , HLT '89, 1989.
DOI : 10.3115/100964.100998

S. Huet, P. Sébillot, &. G. Gravier, S. Mou, V. Seneff et al., « Integration of Supra-Lexical Linguistic Models with Speech Recognition Using Shallow Parsing and Finite State Transducers, Proc. of the 7th International Conference on Spoken Language Processing (ICSLP), 2002.

L. [. Nivre and . Grönqvist, Tagging a Corpus of Spoken Swedish, International Journal of Corpus Linguistics, vol.6, issue.1, pp.47-78, 2001.
DOI : 10.1075/ijcl.6.1.03niv

]. T. Nw96a, P. C. Niesler, and . Woodland, « Combination of Word-Based and Category-Based Language Models, Proc. of the 4th International Conference on Spoken Language Processing (ICSLP), 1996.

]. T. Nw96b, P. C. Niesler, and . Woodland, « A Variable-Length Category-Based N- Gram Language Model, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1996.

E. [. Niesler, P. C. Whittaker, and . Woodland, Comparison of part-of-speech and automatically derived category-based language models for speech recognition, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 1998.
DOI : 10.1109/ICASSP.1998.674396

H. [. Ortmanns, X. Ney, and . Aubert, A word graph algorithm for large vocabulary continuous speech recognition, Computer Speech & Language, vol.11, issue.1, pp.43-72, 1997.
DOI : 10.1006/csla.1996.0022

]. D. Pal03 and . Pallett, « A Look at NIST's Benchmark ASR Tests: Past, Present, and Future, Proc. of the IEEE Workshop Automatic Speech Recognition and Understanding, St. Thomas, ? ?les Vierges, 2003.

S. [. Pallaud and . Henry, Amorces de mots et répétitions : des hésitations plus que des erreurs en français parlé, Actes des 7` emes Journées internationales d'Analyse statistique des Données Textuelles (JADT), 2004.

E. [. Perraud, C. Morin, P. Viard-gaudin, and . Lallican, « Modèles N-grammes et N-classes pour la reconnaissance de l'´ ecriture manuscrite enligne, Traitement Automatique des Langues (TAL), vol.44, issue.1, pp.63-92, 2003.

]. A. Pol03 and . Polgù-ere, Lexicologie et sémantique lexicale : notions fondamentales. Les Presses de l, 2003.

E. [. Panunzi, M. Picchi, and . Moneglia, « Using PiTagger for Lemmatization and PoS Tagging of a Spontaneous Speech Corpus: C-Oral-Rom Ital- Irisa Utilisation de la linguistique en reconnaissance de la parole 69

. Ian, [PS01] F. Peng et D. Schuurmans. « A Simple Closed-Class/Open-Class Factorization for Language Modeling, Proc. of the 4th International Conference on Language Resources and Evaluation (LREC) Proc. of the 6th Natural Language Processing Pacific Rim Symposium (NLPRS), 2001.

]. L. Rab89 and . Rabiner, « A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. of the IEEE, pp.257-285, 1989.

F. [. Ries, A. Buø, and . Waibel, Class phrase models for language modeling, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96, 1996.
DOI : 10.1109/ICSLP.1996.607138

]. R. Ros94 and . Rosenfeld, « A Hybrid Approach to Adaptive Statistical Language Modeling, Proc. of the ARPA Workshop on Human Language Technology, 1994.

]. R. Ros96 and . Rosenfeld, « A Maximum Entropy Approach to Adaptive Statistical Language Modeling, Computer, Speech and Language, vol.10, pp.187-228, 1996.

]. R. Ros00a and . Rosenfeld, « Incorporating Linguistic Structure into Statistical Language Models, Philosophical Transactions: Mathematical, Physical and Engineering Sciences, vol.358, pp.1311-1324, 2000.

]. R. Ros00b and . Rosenfeld, « Two Decades of Statistical Language Modeling: Where do we Go from Here?, Proc. of the IEEE, pp.1270-1278, 2000.

S. [. Schwartz, . Austin, and . Efficient, Efficient, high-performance algorithms for N-Best search, Proceedings of the workshop on Speech and Natural Language , HLT '90, 1990.
DOI : 10.3115/116580.116581

[. Su, T. Chiang, and Y. Lin, « A Unified Framework to Incorporate Speech and Language Information in Spoken Language Processing, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992.

J. [. Schwenk and . Gauvain, « Neural Network Language Models for Conversational Speech Recognition, Proc. of the 8th International PI n?1804 70 S. Huet, P. Sébillot & G. Gravier Conference on Spoken Language Processing (ICSLP), ? ?le de Jeju, Corée du SudShr94] E. Shriberg. « Preliminaries to a Theory of Speech Disfluencies Thèse de doctorat, 1994.

]. E. Shr01, . Shriberg, and . To, Errrr " is Human: Ecology and Acoustics of Speech Disfluencies », Journal of the International Phonetic Association, vol.31, issue.1, pp.153-169, 2001.

M. [. Seneff, V. Mccandless, and . Zue, « Integrating Natural Language into the Word Graph Search for Simultaneous Speech Recognition and Understanding, Proc. of the 4th European Conference on Speech Communication and Technology (Eurospeech), 1995.

[. Siu and M. Ostendorf, « Modeling Disfluencies in Conversational Speech, Proc. of the 4th International Conference on Spoken Language Processing Siu et M. Ostendorf. « Variable N-Grams and Extensions for Conversational Speech Language Modeling ». IEEE Transactions on Speech and Audio Processing, pp.63-75, 1996.

R. [. Seymore and . Rosenfeld, « Using Story Topics for Language Model Adaptation, Proc. of the 5th European Conference on Speech Communication and Technology (Eurospeech), 1997.

W. [. Samuelsson, A class-based language model for large-vocabulary speech recognition extracted from part-of-speech statistics, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)
DOI : 10.1109/ICASSP.1999.758181

J. [. Stolcke and . Segal, -gram probabilities from stochastic context-free grammars, Proceedings of the 32nd annual meeting on Association for Computational Linguistics -, 1994.
DOI : 10.3115/981732.981743

URL : https://hal.archives-ouvertes.fr/hal-01194269

E. [. Stolcke and . Shriberg, Statistical language modeling for speech disfluencies, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, 1996.
DOI : 10.1109/ICASSP.1996.541118

]. A. Sto95 and . Stolcke, « An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities », Computational Linguistics, vol.21, issue.2, pp.165-202, 1995.

]. A. Irisa-[-sto97, S. Stolcke, C. Seneff, T. J. Wang, and . Hazen, Suhm et A. Waibel. « Towards Better Language Models for Spontaneous Speech « Automatic Induction of N-Gram Language Models from a Natural Language Grammar, Linguistic Knowledge and Empirical Methods in Speech Recognition -Natural Language Processing ». AI Magazine 1997. [Str03] S. Strassel. « Simple Metadata Annotation Specification. Version 5.0 ». Linguistic Data Consortium Proc. of the 3rd International Conference on Spoken Language Processing (ICSLP) Proc. of the 8th European Conference on Speech Communication and Technology (Eurospeech ), 1994.

M. Tamoto, T. Kawabata, K. Uchimoto, C. Nobata, A. Yamada et al., « Morphological Analysis of the Spontaneous Speech Corpus « Internet Documents: a Rich Source for Spoken Language Modeling « Morphology-Based Language Modeling for Arabic Speech Recognition, Clustering Word Category Based on Binomial Posteriori Cooccurrence Distribution Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing Proc. of the Workshop Computational Natural Language Learning (CoNLL 97 Proc. of the 19th International Conference on Computational Linguistics (COLING) Proc. of the IEEE Workshop Automatic Speech Recognition and Understanding (ASRU)VEZD00] F. Van Eynde, J. Zavrel et W. Daelemans. « Part of Speech Tagging and Lemmatisation for the Spoken Dutch Corpus Proc. of the Conference on Language Resources and Evaluation (LREC) Proc. of the 8th International Conference on Spoken Language Processing (ICSLP), ? ?le de Jeju, Corée du SudVV99] A. Valli et J. Véronis. « ´ Etiquetage grammatical de corpus oraux : probì emes et perspectives ». Revue française de linguistique appliquéeWH02] W. Wang et M. P. Harper. « The SuperARV Language Model: Investigating the Effectiveness of Tightly Integrating Multiple Knowledge Sources, pp.113-133, 1995.

M. [. Wang, A. Harper, and . Stolcke, The robustness of an almost-parsing language model given errorful training data, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003.
DOI : 10.1109/ICASSP.2003.1198762

S. [. Wu and . Khudanpur, « Combining Nonlocal, Syntactic and N-Gram Dependencies in Language Modeling, Proc. of the 5th European Conference on Speech Communication and Technology (Eurospeech), 1999.

Y. [. Wang, M. P. Liu, and . Harper, Rescoring effectiveness of language models using different levels of knowledge and their integration, IEEE International Conference on Acoustics Speech and Signal Processing, 2002.
DOI : 10.1109/ICASSP.2002.5743835

M. Wang, X. Mahajan, and . Huang, « A Unified Context-Free Grammar and N-Gram Model for Spoken Language Processing, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2000.

A. [. Wang, M. P. Stolcke, and . Harper, The use of a linguistically motivated language model in conversational speech recognition, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004.
DOI : 10.1109/ICASSP.2004.1325972

P. [. Whittaker and . Woodland, Efficient class-based language modelling for very large vocabularies, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001.
DOI : 10.1109/ICASSP.2001.940889

Y. [. Yamamoto and . Sagisaka, Multi-class composite N-gram based on connection direction, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999.
DOI : 10.1109/ICASSP.1999.758180

A. [. Zechner and . Waibel, « Using Chunk Based Partial Parsing of Spontaneous Speech in Unrestricted Domains for Reducing Word Error Rate in Speech Recognition, Proc. of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics (COLING-ACL), 1998.