Bayesian class-based language models

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2011, Pages 5564-5567

  Su, Yi ^a  

^a NUANCE COMMUNICATIONS   (United States)

Author keywords

Bayesian statistics; class based language model; Gibbs sampling; hierarchical Pitman Yor process

Indexed keywords

BAYESIAN FORMULATION; BAYESIAN STATISTICS; BUSINESS PRODUCTS; CLASS-BASED LANGUAGE MODEL; GIBBS SAMPLERS; GIBBS SAMPLING; HIERARCHICAL PITMAN-YOR PROCESS; MULTIPLE CLASS; RELATIVE REDUCTION; SIMPLIFYING ASSUMPTIONS; SPEECH RECOGNIZER; WALL STREET JOURNAL; WORD ERROR RATE;

COMPUTATIONAL LINGUISTICS; SIGNAL PROCESSING; SPEECH RECOGNITION;

SPEECH COMMUNICATION;

EID: 80051605347     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5947620     Document Type: Conference Paper

References (12)

1. P. F. Brown, V. J. D. Pietra, P. V. deSouza, J. C. Lai, and R. L. Mercer, "Class-based n-gram models of natural language," Computational Linguistics, vol. 18, no. 4, pp. 467-479, 1992.
2. L. Saul and F. Pereira, "Aggregate and mixed-order Markov models for statistical language processing," in Proceedings of the Conference on Empirical methods in natural language processing, 1997.
3. D. J. C. MacKay and L. C. B. Peto, "A hierarchical Dirichlet language model," Natural Language Engineering, vol. 1, no. 3, pp. 1-19, 1994.
4. Y. W. Teh, "A hierarchical Bayesian language model based on Pitman-Yor processes," in Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, 2006, pp. 985-992.
5. H. M. Wallach, "Topic modeling: beyond bag-of-words," in Proceedings of the 23rd International Conference on Machine Learning, 2006, pp. 977-984.
6. S. Martin, J. Liermann, and H. Ney, "Algorithms for bigram and trigram word clustering," Speech Communication, vol. 24, no. 1, pp. 19-37, 1998.
7. P. Xu and F. Jelinek, "Random forests in language modeling," in Proceedings of 2004 Conference on Empirical Methods in Natural Language Processing, D. Lin and D. Wu, Eds. Barcelona, Spain: Association for Computational Linguistics, 2004, pp. 325-332.
8. A. Emami and F. Jelinek, "Random clusterings for language modeling," in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, March 2005, pp. 581-584.
9. T. L. Griffiths and M. Steyvers, "Finding scientific topics," Proceedings of the National Academy of Sciences of the United States of America, vol. 101, pp. 5228-5235, April 2004.
10. H. Ney, U. Essen, and R. Kneser, "On structuring probabilistic dependencies in stochastic language modelling," Computer Speech and Language, vol. 8, pp. 1-38, 1994.
11. Y. W. Teh, "A Bayesian interpretation of interpolated Kneser-Ney," School of Computing, National University of Singapore, Tech. Rep. TRA2/06, 2006.
12. H. Soltau, B. Kingsbury, L. Mangu, D. Povey, G. Saon, and G. Zweig, "The IBM 2004 conversational telephony system for rich transcription," in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, pp. 205-208.