Duration-distribution-based HMM for speech recognition

Frontiers of Electrical and Electronic Engineering in China

Volumn 1, Issue 1, 2006, Pages 26-30

  Wang, Zuo Ying ^a   Xiao, Xi ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

DDBHMM; Duration; Speech recognition

Indexed keywords

EID: 33746080038     PISSN: 16733460     EISSN: 16733584     Source Type: Journal    
DOI: 10.1007/s11460-005-0010-z     Document Type: Article

References (19)

1. Rabiner, L. R., A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, 1989, 77(2): 257-286
2. Levision S. E., Rabiner L. R. and Sondhi M. M., An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition, Bell Syst. Tech. J., 1983, 62(4): 1035-1074
3. Qi S. and Zhang J., A study of duration of Chinese consonants, ACAT Acustica, 1982, 7(1): 8-13 (in Chinese)
4. Burshtein D., Robust parametric modeling of durations in hidden Markov models, IEEE Proc. ICASSP, 1995, 1: 548-551
5. Hung W.-W. and Wang H.-C., Improvement of noisy speech recognition using a proportional alignment decoding algorithm in the training phase, Comput. Speech Lang., 1998, 12(3): 165-192
6. Ferguson J. D., Variable duration models for speech, Proceedings of the Symposium on the Application of Hidden Markov Models to Text and Speech, 1980, 143-179
7. Levinson S. E., Continuously variable duration hidden Markov models for automatic speech recognition, Comput, Speech Lang., 1986, 1: 29-45
8. Russell M. J. and Moore R. K., Explicit modeling of state occupancy in hidden Markov models for automatic speech recognition, IEEE Proc. ICASSP, 1985, 5-8
9. Ratnayake N., Savic M. and Sorensen J., Use of semi-Markov Models for speaker-independent phoneme recognition, IEEE ICASSP, 1992, 1: 565-568
10. Russel M. J. and Cook A. E., Experimental evaluation of duration modeling techniques for automatic speech recognition, IEEE Proc. ICASSP, 1987, 2376-2379
11. Neri M. and Yariv E., Hidden Markov modeling using the most likely state sequence, IEEE Proc. ICASSP, 1991, 469-472
12. Ramesh P. and Wilpon J. G., Modeling state durations in hidden Markov models for automatic speech recognition, IEEE Proc. ICASSP, 1992, 1: 381-384
13. Mitchell C. D. and Jamieson L. H., Modeling duration in a hidden Markov model with the exponential family, IEEE Proc. ICASSP, 1993, 2: 331-334
14. Bonafonteu A., Vidal J. and Nogueiras A., Duration modeling with expanded HMM applied to speech recognition, Proc. ICSLP 96, Fourth International Conference on Spoken Language, 1996, 2: 1097-1100
15. Wang Z., Duration distribution based HMM speech recognition models, The Second National Conference on Chinese Characters and Speech Recognition, 1989, Lushan, China (in Chinese)
16. Vaseghi S. V., Hidden Markov models with duration-dependent state transition probabilities, Electron. Lett., 1991, 27(8): 625-626
17. Vaseghi S. V., State duration modeling in hidden Markov models, Signal Process., 1995, 41: 31-41
18. Ostendorf M., Digalakis V. V. and Kimball O. A., From HMM's to segment models: a unified view of stochastic modeling for speech recognition, IEEE Trans. Speech Audio Process., 1996, 14(5): 360-378
19. Wang Z. and Gao H., An inhomogeneous HMM speech recognition algorithm, Chin, J. Electron., 1998, 7(1): 73-77