Toward robust learning of the Gaussian mixture state emission densities for hidden Markov models

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

Volumn , Issue , 2010, Pages 5242-5245

  Tang, Hao ^a   Hasegawa Johnson, Mark ^a   Huang, Thomas S ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Baum Welch algorithm; Boosting; Gaussian mixture density; Hidden Markov model

Indexed keywords

EMOTION RECOGNITION; GAUSSIAN DISTRIBUTION; LEARNING ALGORITHMS; PROBABILITY DENSITY FUNCTION; SPEECH RECOGNITION; TRELLIS CODES;

BAUM-WELCH ALGORITHMS; BOOSTING; EMISSION-DENSITY; FREE PARAMETERS; GAUSSIAN MIXTURE DENSITIES; GAUSSIAN-MIXTURES; HIDDEN-MARKOV MODELS; LEARN+; ROBUST LEARNING; TRAINING DATA;

HIDDEN MARKOV MODELS;

EID: 78049386242     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2010.5494989     Document Type: Conference Paper

References (24)

1. L.R. Rabiner, "A tutorial on Hidden Markov Models and selected applications in speech recognition," Proceedings of the IEEE, 77 (2): 257-286, 1989.
2. C.M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
3. L.R. Rabiner and B.H. Juang. Fundamentals of Speech Recognition. Prentice Hall, Upper Saddle River, N.J., 1993.
4. C. Manning and H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press. Cambridge, MA: May 1999.
5. J. Hu, M. Brown, W. Turin, "HMM Based On-Line Handwriting Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10):1039-1045, 1996.
6. L.E. Baum, T. Petrie, G. Soules, and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains," Ann. Math. Statist., 41(1):164-171, 1970.
7. G.J. McLachlan, D. Peel. Finite Mixture Models. Wiley, 2000.
8. R.O. Duda, P.E. Hart, D.G. Stork. Pattern classification (2nd edition). Wiley, New York, 2001.
9. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, 55(1):119-139, 1997.
10. B. Schuller, S. Steidl, A. Batliner, "The Interspeech 2009 Emotion Challenge," Interspeech, Brighton, UK, 2009.
11. D. Povey, S. Chu, B. Varadarajan, "Universal Background Model Based Speech Recognition," ICASSP 2008.
12. R. Berk, "An Introduction to EnsembleMethods for Data Analysis," Department of Statistics Papers, UCLA, 2005.
13. L. Mason, J. Baxter, P. Bartlett, M. Frean, "Boosting algorithms as gradient descent," NIPS 1999.
14. S. Rosset, E. Segal, "Boosting density estimation," NIPS 2002.
15. F. Wang, C. Zhang and N. Lu. Boosting GMM and its two applications. MCS 2005.
16. H. Tang, S. Chu, M. Hasegawa-Johnson, T. Huang. "Emotion Recognition from Speech via Boosted Gaussian Mixture Models," ICME 2009.
17. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm." J. of Royal Stat. Society, B, 39(1):1-38, 1977.
18. D. Seppi, A. Batliner, B. Schuller, S. Steidl, T. Vogt, J.Wagner, L. Devillers, L. Vidrascu, N. Amir, and V. Aharonson, "Patterns, Prototypes, Performance: Classifying Emotional User States," Interspeech, Brisbane, Australia, 2008.
19. D. Ververidis, C. Kotropoulos, "Emotional speech recognition: resources, features, and methods," Speech Communication 48(9):1162-1181, 2006.
20. D. Neiberg, K. Elenius, and K. Laskowski, "Emotion recognition in spontaneous speech using GMMs," Interspeech 2006.
21. C. Lee, S. Yildirim, M. Bulut, A. Kazemzadeh, C. Busso, Z. Deng, S. Lee, S. Narayanan, "Emotion recognition based on phoneme classes," ICSLP 2004.
22. T.L. Nwe, S.W. Foo and L.C. De Silva. Speech emotion recognition using hidden markov models. Speech Communication 41, pp. 603-23, 2003.
23. H. Tang and T. Huang, "Boosting Gaussian Mixture Models via Discriminant Analysis," 2008 IEEE International Conference on Pattern Recognition, Tempa, FL, December, 2008.
24. Harvard Sentences: From the appendix of: IEEE Subcommittee on Subjective Measurements IEEE Recommended Practices for Speech Quality Measurements. IEEE Transactions on Audio and Electroacoustics. vol 17, 227-46, 1969.