A speech recognition method based on the sequential multi-layer perceptrons

Neural Networks

Volumn 9, Issue 4, 1996, Pages 655-669

  Chen, Wen Yuan ^a   Chen, Sin Horng ^b   Lin, Cheng Jung ^b  

^a INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Dynamic programming; Generalized probabilistic descent; Hidden Markov methods; Multi layer perceptrons; Neural network; Speech recognition

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DYNAMIC PROGRAMMING; MARKOV PROCESSES; SPEECH RECOGNITION;

GENERALIZED PROBABILISTIC DESCENT ALGORITHM; HIDDEN MARKOV MODELS; MANDARIN DIGITS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; PRIORITY JOURNAL; SPEECH DISCRIMINATION;

EID: 0030176411     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/0893-6080(95)00140-9     Document Type: Article

References (40)

1. Aikawa, K. (1991). Speech recognition using time-warping neural networks. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (pp. 337-346).
2. Amari, S. (1967). A theory of adaptive pattern classifiers. IEEE Transactions on Electronic Computers, 16, 299-307.
3. Austin, S., Zavaliagkos, G., Makhoul, J., & Schwartz, R. (1991). A hybrid continuous speech recognition system using segmental neural nets with hidden Markov models. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (pp. 347-356).
4. Bladon, A. (1985). Acoustic phonetics, auditory phonetics, speaker sex, and speech recognition: A thread. In F. Fallside (Ed.), Computer speech processing (p. 29). Englewood Cliffs, NJ: Prentice-Hall.
5. Bourlard, H., & Wellekens, C. J. (1990). Links between Markov models and multilayer perceptrons. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 1167-1178.
6. Bourlard, H., Morgan, N., Wooters, C., & Renals, S. (1992). CDNN: a context dependent neural networks for continuous speech recognition. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. II-349-II-352).
7. Bridle, J. S. (1990). ALPHA-nets: a recurrent neural network architecture with a hidden Markov model interpretation. Speech Communication, 9, 83-92.
8. Cerf, P. L., Ma, W., & Compernolle, D. V. (1994). Multilayer perceptrons as labelers for hidden Markov models. IEEE Transactions on Speech and Audio Processing, 2, 185-193.
9. Chang, P. C., & Juang, B. H. (1993). Discriminative training of dynamic programming based speech recognizers. IEEE Transactions on Speech and Audio Processing, 1, 135-143.
10. Chen, W. Y., & Chen, S. H. (1991). Word recognition based on the combination of a sequential neural network and the GPDM discriminative training algorithm. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (pp. 376-384).
11. Dautrich, B. A., Rabiner, L. R., & Martin, T. B. (1983). On the effects of varying filter bank parameters on isolated word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 31, 793-803.
12. Devijver, P. A., & Kittler, J. (1982). Pattern recognition. London: Prentice Hall International.
13. Hampshire, J. B., & Waibel, A. H. (1990). A novel objection function for improved phoneme recognition using time-delay neural networks. IEEE Transactions on Neural Networks, 1, 216-228.
14. Hassanein, K., Deng, L., & Elmasry, M. I. (1994). Vowel classification using a neural predictive HMM: a discriminative training approach. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. II-665-II-668).
15. Hush, D. R., & Horne, B. G. (1993). Progress in supervised neural networks. IEEE Signal Processing Magazine, 10, 8-39.
16. Juang, B. H., & Katagiri, S. (1992). Discriminative training. The Journal of the Acoustical Society of Japan (E), 13, 333-339.
17. Katagiri, S., Lee, C. H., & Juang, B. H. (1991). New discriminative training algorithms based on the generalized probabilistic descent method. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (pp. 299-308).
18. Kohonen, T., Barna, G., & Chrisley, R. (1988). Statistical pattern recognition with neural networks: benchmarking studies. Proceedings of the IEEE International Conference on Neural Networks (pp. 61-68).
19. Komori, T., & Katagiri, S. (1992). GPD training of dynamic programming-based speech recognizers. The Journal of the Acoustical Society of Japan (E), 13, 341-349.
20. Lang, K. J., & Waibel, A. H. (1990). A time-delay neural network architecture for isolated word recognition. Neural Networks, 3, 23-43.
21. Liou, J. S., Chen, R. G., Yu, S. M., Hwang, J. R., & Jou, I. C. (1990). The speech database of telecommunication Laboratories, Ministry of Transportation and Communications, ROC. Proceedings of the Telecommunications Symposium, Taiwan, (pp. 128-132).
22. Morgan, N., & Bourlard, H. (1990). Continuous speech recognition using multilayer perceptrons with hidden Markov models. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 413-416).
23. Niles, L. T., & Silverman, H. F. (1990). Combining hidden Markov model and neural networks classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 417-420).
24. Pao, Y. H. (1989). Adaptive pattern recognition and neural networks. Reading, MA: Addison-Wesley.
25. Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77, 257-286.
26. Rainton, D., & Sagayama, S. (1992). Minimum error classification training of HMMs-implementation details and experimental results. The Journal of the Acoustical Society of Japan (E), 13, 379-388.
27. Reichl, W., Caspary, P., & Ruske, G. (1994). A new model-discriminant training algorithm for hybrid NN-HMM systems. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. II-677-II-680).
28. Renals, S., Morgan, N., Cohen, M., & Franco, H. (1992). Connectionist probability estimation in DECIPHER speech recognition system. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. I-601-I-604).
29. Renals, S., Morgan, N., Bourlard, H., Cohen, M., & Franco, H. (1994). Connectionist probability estimators in HMM speech recognition. IEEE Transactions on Speech, and Signal Processing, 2, 161-174.
30. Rigoll, G. (1994). Maximum mutual information neural networks for hybrid connectionist-HMM speech recognition systems. IEEE Transactions on Speech and Audio Processing, 2, 175-184.
31. Rumelhart, D. E. (1986). Learning internal representation by error propagation. In D. E. Rumelhart, G. E. Hinton, & R. J. Williams (Eds.), Parallel distributed processing: exploration in the microstructure of cognition (pp. 318-362). Cambridge, MA: MIT Press.
32. Sakoe, H., Isotani, R., Yoshida, K., Iso, K., & Watanabe, T. (1989). Speaker-independent word recognition using dynamic programming neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 29-32).
33. Schwartz, R., & Chow, Y. L. (1990). The n-best algorithm: an efficient and exact procedure for finding the n most likely sentence hypotheses. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 81-84).
34. Soong, F. K., & Huang, E. F. (1991). A tree trellis based fast search for finding the n best sentence hypotheses in continuous speech recognition. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 705-708).
35. Svendsen, T., & Soong, F. K. (1987). On the automatic segmentation of speech signals. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 77-80).
36. Tebelskis, J., & Waibel, A. (1991). Continuous speech recognition using linked predictive neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 61-64).
37. Villiers, J. D., & Barnard, E. (1993). Backpropagation neural nets with one and two hidden layers. IEEE Transactions on Neural Networks, 4, 136-141.
38. Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989). Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37, 328-339.
39. Watrous, R. L. (1990). Phoneme discrimination using connectionist networks. The Journal of the Acoustical Society of America, 87, 1753-1771.
40. Ye, H., Wang, S., & Robert, F. (1990). A pcmn neural network for isolated word recognition. Speech Communication, 9, 141-153.