Unsupervised language model adaptation using latent Dirichlet allocation and dynamic marginals

European Signal Processing Conference

Volumn , Issue , 2011, Pages 1480-1484

  Haidar, Md Akmal ^a   O'Shaughnessy, Douglas ^a  

^a UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND MODEL; KL-DIVERGENCE; KULLBACK-LEIBLER DIVERGENCE; LANGUAGE MODEL ADAPTATION; LATENT DIRICHLET ALLOCATION; LATENT DIRICHLET ALLOCATIONS; MARGINALS; N-GRAMS; TEXT DATA; TOPIC MODEL; WORD ERROR RATE;

COMPUTATIONAL LINGUISTICS; PROBABILITY DISTRIBUTIONS; SIGNAL PROCESSING; STATISTICS;

MODELS;

EID: 84863739523     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. J. R. Bellegarda, "Statistical Language Model Adapta-tion: Review and perspective", Speech Communica-tion, vol. 42, pp. 93-108, 2004.
2. R. Kneser and V. Steinbiss, "On the Dynamic Adapta-tion of Stochastic Language Models", in Proc. of ICASSP, vol. 2, pp. 586-589, 1993.
3. J. R. Bellegarda, "Exploiting Latent Semantic Information in Statistical Language Modeling", in IEEE Trans. on Speech and Audio Proc, vol. 88, No. 8, pp. 1279-1296, 2000.
4. D. Gildea and T. Hofmann, "Topic-Based Language Models Using EM", in Proc. of EUROSPEECH, pp. 2167-2170, 1999.
5. D. M. Blei, A. Y.Ng, and M. I. Jordan, "Latent Dirich-let Allocation", Journal of Machine Learning Re-search, vol. 3, pp. 993-1022, 2003.
6. Y.-C. Tam and T. Schultz, "Unsupervised Language Model Adaptation Using Latent Semantic Marginals", in Proc. of INTERSPEECH, pp. 2206-2209, 2006.
7. R. Kneser, J. Peters, and D. Klakow, "Language Mod-el Adaptation Using Dynamic Marginals", in Proc. of EUROSPEECH, pp. 1971-1974, 1997.
8. M. A. Haidar and D. O'Shaughnessy, "Unsupervised Language Model Adaptation Using N-gram weight-ing", in Proc. of CCECE, 2011.
9. http://jmlr.csail.mit.edu/papers/volume5/lewis04a/a11-smart-stop-list/ english.stop
10. R. Kuhn and R. D. Mori, "A Cache-Based Natural Language Model for Speech Recognition", in IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 12(6), pp. 570-583, 1990.
11. R. Rosenfeld, "A Maximum Entropy Approach to Adaptive Statistical Language Modeling", Computer, Speech and Language, vol. 10(3), pp. 187-228, 1996.
12. R. Iyer and M. Ostendorf, "Modeling Long Distance Dependence in Language: Topic Mixtures vs Dynamic Cache Models", in Proc. of ICSLP, vol. 1, pp. 236-239, 1996.
13. Y.-C. Tam and T. Schultz, "Dynamic Language Mod-el Adaptation Using Variational Bayes Inference", in Proc. of INTERSPEECH, pp. 5-8, 2005.
14. F. Liu and Y. Liu, "Unsupervised Language Model Adaptation Incorporating Named Entity Information", in Proc. of ACL, pp. 672-679, 2007.
15. M. A. Haidar and D. O'Shaughnessy, "Novel Weight-ing Scheme for Unsupervised Language Model Adap-tation Using Latent Dirichlet Allocation", in Proc. of INTERSPEECH, pp. 2438-2441, 2010.
16. A. Sethy and B. Ramabhadran, "Bag-Of-Word Nor-malized N-gram Models", in Proc. of INTERSPEECH, pp. 1594-1597, 2008.
17. T. L. Griffiths and M. Steyvers, "Finding Scientific Topics", in Proc. National Academy of Sciences, 101 (suppl. 1), pp. 5228-5235, 2004.
18. A. Stolcke, "SRILM- An Extensible Language Model-ing Toolkit", in Proc. of ICSLP, vol. 2, pp. 901-904, 2002.
19. S. Young, P. Woodland, G. Evermann and M. Gales, "The HTK toolkit 3.4.1", http://htk.eng.cam.ac.uk/, Cambridge Univ. Eng. Dept. CUED.
20. K. Vertanen, "HTK Wall Street Journal Training Rec-ipe", http://www.inference.phy.cam.ac.uk/kv227/htk/.