A Study on Model-Based Error Rate Estimation for Automatic Speech Recognition

IEEE Transactions on Speech and Audio Processing

Volumn 11, Issue 6, 2003, Pages 581-589

  Huang, Chao Shih ^a,b   Wang, Hsiao Chuan ^c   Lee, Chin Hui ^d  

^a Innovation Laboratory   (Netherlands)

^b Acer Inc   (Taiwan)

^c NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^d GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Automatic speech recognition (ASR); Divergence; Error rate estimation; Hidden Markov model (HMM); Model based misclassification measure

Indexed keywords

ALGORITHMS; ERROR ANALYSIS; LEARNING SYSTEMS; MARKOV PROCESSES; PROBABILITY;

AUTOMATIC SPEECH RECOGNITION (ASR); ERROR RATE ESTIMATION; HIDDEN MARKOV MODEL (HMM);

SPEECH RECOGNITION;

EID: 0347338005     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSA.2003.818030     Document Type: Article

References (30)

1. A. Bhattacharyya, "On a measure of divergence between two statistical populations defined by their probability distributions," Bull. Calcutta Math. Soc., vol. 35, pp. 99-110, 1943.
2. K. W. Bowyer and P. J. Phillips, Eds., Empirical Evaluation Techniques in Computer Vision. Los Alamitos, CA: IEEE Comput. Soc. Press, 1998.
3. H. Chemoff, "A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations," Ann. Math. Statist., vol. 23, pp. 493-507, 1952.
4. W. Chou, B.-H. Juang, and C.-H. Lee, "Segmental GPD training of HMM based speed recognizer," in Proc. ICASSP-92, 1992, pp. 473-476.
5. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: McGraw-Hill, 1967.
6. T. M. Cover, "Learning in pattern recognition," in Methodologies of Pattern Recognition, S. Watanabe, Ed. New York: Academic, 1969, pp. 111-132.
7. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Statist. Soc. B, vol. 39, pp. 1-38, 1977.
8. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York: Wiley, 2000.
9. B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans. Philadelphia, PA: SIAM, 1982.
10. Y. Ephraim, A. Dembo, and L. R. Rabiner, "A minimum discrimination information approach for hidden Markov modeling," IEEE Trans. Inform. Theory, vol. 35, pp. 1001-1013, Sept. 1989.
11. Y. Ephraim, H. Lev-Ari, and R. M. Gray, "Asymptotic minimum discrimination information measure for asymptotically weakly stationary process," IEEE Trans. Inform. Theory, vol. 34, pp. 1033-1040, Sept. 1988.
12. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. New York: Academic, 1990.
13. M. Gales and S. Young, "Cepstral parameter compensation for HMM recognition in noise," Speech Commun., vol. 12, pp. 231-239, July 1993.
14. R. M. Gray, A. H. Gray Jr., G. Rebolledo, and J. E. Shore, "Rate-distortion speech coding with a minimum discrimination information measure," IEEE Trans. Inform. Theory, vol. 27, pp. 708-721, Nov. 1981.
15. I. Guyon, J. Makhoul, R. Schwartz, and V. Vapnik, "What size test set gives good error rate estimates?," IEEE Trans. Pattern Recognit. Machine Intell., vol. 20, no. 1, pp. 52-64, 1998.
16. C.-S. Huang, H.-C. Wang, and C.-H. Lee, "An SNR-incremental stochastic matching algorithm for noisy speech recognition," IEEE Trans. Speech Audio Processing, vol. 9, pp. 866-873, Nov. 2001.
17. A. K. Jain, R. C. Dubes, and C.-C. Chen, "Bootstrap techniques for error estimation," IEEE Trans. Pattern Recognit. Machine Intell., vol. 9, no. 5, pp. 628-633, 1998.
18. B.-H. Juang and L. R. Rabiner, "A probabilistic distance measure for hidden Markov models," AT&T Tech. J., vol. 64, no. 2, pp. 391-408, Feb. 1985.
19. B.-H. Juang and L. R. Rabiner, "Mixture autoregressive hidden Markov models for speech signals," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-33, no. 6, pp. 1404-1413, 1985.
20. B.-H. Juang and S. Katagiri, "Discriminative learning for minimum error classification," IEEE Trans. Signal Processing, vol. 40, pp. 3043-3054, 1992.
21. S. Kullback, Information Theory and Statistics. New York: Dover, 1968.
22. C.-H. Lee, "On stochastic feature and model compensation approaches to robust speech recognition," Speech Commun., vol. 25, pp. 29-47, Aug. 1998.
23. P. J. Moreno, "Speech Recognition in Noisy Environments," Ph.D. dissertation, Carnegie Mellon Univ., Pittsburgh, PA, May 1996.
24. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, pp. 257-286, Feb. 1989.
25. L. R. Rabiner, B.-H. Huang, and C.-H. Lee, "An overview of automatic speech recognition," in Automatic Speech and Speaker Recognition: Advanced Topics, C.-H. Lee, F. K. Song, and K. K. Paliwal, Eds. Norwell, MA: Kluwer, 1996, ch. 1.
26. D. A. Reynolds and R. C. Rose, "Robust text-independent speaker identification using Gaussian mixture speaker models," IEEE Trans. Speech Audio Processing, vol. 3, pp. 72-83, 1995.
27. A. E. Rosenberg, J. Delong, C.-H. Lee, B.-H. Juang, and F. K. Soong, "The use of cohort normalized scores for speaker recognition," in Proc. ICSLP-92. New York, Oct. 1992, pp. 599-602.
28. A. Sankar and C.-H. Lee, "A maximum likelihood approach to stochastic matching for robust speech recognition," IEEE Trans. Speech Audio Processing, vol. 4, pp. 190-202, May 1996.
29. A. Varga and H. J. M. Steeneken, "Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems," Speech Commun., vol. 12, pp. 247-251, 1993.
30. A. Viterbi, "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm," IEEE Trans. Inform. Theory, vol. IT-13, pp. 260-269, Apr. 1967.