A novel learning method for hidden Markov models in speech and audio processing

2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006

Volumn , Issue , 2006, Pages 80-85

  He, Xiaodong ^a   Deng, Li ^a   Chou, Wu ^b  

^a MICROSOFT RESEARCH   (United States)

^b AVAYA LABS RESEARCH   (United States)

Author keywords

Discriminative learning; Extended Baum Welch algorithm; Growth transformation; Hidden Markov model; Machine learning; Pattern recogntion; Rational function optimization; Speech recognition and audio processing

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; GRADIENT METHODS; HIDDEN MARKOV MODELS; LEARNING SYSTEMS; MULTIMEDIA SYSTEMS; OPTIMIZATION; PARAMETER ESTIMATION;

DISCRIMINATIVE LEARNING; EXTENDED BAUM-WELCH ALGORITHM; GROWTH TRANSFORMATION; RATIONAL FUNCTION OPTIMIZATION;

SPEECH RECOGNITION;

EID: 34250764726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MMSP.2006.285273     Document Type: Conference Paper

References (17)

1. P. Brown. The Acoustic Modeling Problem in Automatic Speech Recognition, Ph.D. thesis, Carnegie Mellon University, 1987.
2. W. Chou. "Minimum classification error approach in pattern recognition," in Pattern Recognition in Speech and Language Processing, (W. Chou and B.-H. Juang eds.) 20043, CRC Press, Boca Raton, pp. 1-49.
3. L. Deng, J. Wu, J. Droppo, and A. Acero. "Analysis and comparison of two feature extraction/compensation algorithms," IEEE Signal Processing Letters, Vol. 12, No. 6, June, 2005, pp. 477-480.
4. P. S. Gopalakrishnan, D. Kanevsky, A. Nadas, and D. Nahamoo, "An Inequality for Rational Functions with Applications to Some Statistical Estimation Problems," IEEE Trans. Information Theory., Vol 37, pp. 107-113, January. 1991.
5. A. Gunawardana and W. Byrne, "Discriminative Speaker Adaptation with Conditional Maximum Likelihood Linear Regression," Proc. EUROSPEECH, 2001.
6. X. He and W. Chou, "Minimum Classification Error Linear Regression For Acoustic Model Adaptation of Continuous Density HMMs," Proc. ICASSP, April 2003.
7. B.-H. Juang, and S. Katagiri. "Discriminative Learning for Minimum Error Classification, IEEE, Trans. on SP., Vol. 40, No. 12, 1992, pp. 3043-3054.
8. B.-H. Juang, W. Chou, and C.-H. Lee, "Minimum Classification Error Rate Methods for Speech Recognition," IEEE Trans. Speech Audio Proc., vol. 5, May 1997.
9. W. Macherey, L. Haferkamp, R. Schluter, and H. Ney, "Investigations on error minimizing training criteria for discriminative training in automatic speech Recognition," Proc. Interspeech, Sept., 2005, Lisbon, Portugal, pp. 2133-2136.
10. Y. Normandin, "Hidden Markov Models, Maximum Mutual Information Estimation, and the Speech Recognition Problem," PhD. dissertation, McGill University, Montreal, 1991.
11. D. Povey and P.C. Woodland, "Minimum Phone Error and I-Smoothing for Improved Discriminative Training," Proc. ICASSP, 2002.
12. D. Povey, M.J.F. Gales, D.Y. Kim, and P.C. Woodland, "MMI-MAP and MPE-MAP for Acoustic Model Adaptation," Proc. Eurospeech, September 2003.
13. D. Povey, "Discriminative training for large vocabulary speech recognition," Ph.D. dissertation, Cambridge University, Cambridge, UK, 2003.
14. C. Rathinavalu and L. Deng. "Speech trajectory discrimination using the minimum classification error learning," IEEE Trans. Speech and Audio Processing, Vol.6, No.6, Nov. 1998, pp. 505-515.
15. L. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, New Jersey: Prentice Hall, 1993.
16. J. L. Roux and E. McDermott, "Optimization for Discriminative Training," Proc. INTERSPEECH, 2005.
17. E. McDermott and T. Hazen, "Minimum Classification Error Training of Landmark Models for Real-Time Continuous Speech Recognition," ICASSP04', Proc. May, 2004.