Stochastic trajectory modeling for speech recognition

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

Volumn 1, Issue , 1994, Pages I57-I60

  Gong, Yifan ^a   Haton, Jean Paul ^a  

^a INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

SIGNAL PROCESSING; STOCHASTIC SYSTEMS; TRAJECTORIES;

ALPHABET RECOGNITION; CONTEXT DEPENDENT; GAUSSIAN DENSITY FUNCTION; PARAMETER SPACES; RECOGNITION ACCURACY; SENTENCE RECOGNITION; STATE SEQUENCES; STOCHASTIC TRAJECTORY MODELS;

SPEECH RECOGNITION;

EID: 0003384830     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.1994.389356     Document Type: Conference Paper

References (13)

1. S. Furui. Speaker-independent isolated word recognition using dynamic features of speech spectrum. IEEE Trans. Acoust., Speech and Signal Processing, ASSP- 34(1):53-59, 1986.
2. Wellekens. Explicite time correlation in hidden Markov models for speech recognition. In Proceedings of International Conference on Acoustics, Speech and Signal Processing, pages 384-386, Dallas, 1987.
3. K. K. Paliwal. Use of temporal correlation between successive frames in a hidden Markov model based speech recognizer. In Proceedings of International Conference on Acouatics, Speech and Signal Processing, volume 11, pages 215-218, 1993.
4. S. Takahashi, T. Matsuoka, Y. Miuami, and K. Shikano. Phoneme HMMS constrained by frame correlations. In Proceedings of International Conference on Acoustics, Speech and Signal Processing, volume 11, pages 219-222, 1993.
5. T. Robinson. A real-time recurrent error propagation network word recognition system. In Proceedings of International Conference on Acoustics, Speech and Signal Processing, volume I, pages 617-620, 1992.
6. M. Ostendorf and S. Roucos. A stochastic segment model for phoneme-based continuous speech recognition. IEEE Trans. Acoust., Speech and Signal Processing, 37(12):1857-1869, 1989.
7. V. V. Digalakis, M. Ostendorf, and J. R. Rohlicek. Fast algorithms for phone classification and recognition using segment-based models. IEEE Trans. on Signal Processing, 40(12):2885-2896, Dec. 1992.
8. W. C. Treurniet and Y. Gong. Noise independent speech recognition for a variety of noise types. In IEEE International Conference on Acoustics, Speech and Signal Processing, Adelaide, Australia, April 1994.
9. Y. Linde, A. Buzo, and R. M. Gray. An algorithm for the vector quantizer design. IEEE. Trans. on Communication, COM-28(1):84-95, Jan. 1980.
10. Y. Gong and J.-P. Haton. VINICS: A continuous speech recognizer based on a new robust formulation. In Proceedings of European Conference on Speech Communication and Technology, volume 111, pages 1221-1224, Genova, Italy, September 1991.
11. Y. Gong and J-P.: Haton. Iterative transformation and alignment for speech labeling. In Proceedings of European Conference on Speech Communication and Technology, volume 3, pages 1759-1762, Berlin, Germany, September 1993.
12. H. P. Tseng, M. J. Sabin, and E. A. Lee. Fuzzy vector quantization applied to hidden Markov modeling. In Proc. Int. Conf. on Acoustics, Speech and Signal Processing 1987, pages 641-644, Dallas, Texas, April 1987.
13. C. H. Lee, C. H. Lin, and B. H. Juang. A study on speaker adapt.ation of the parameters of continuous density hidden Markov models. IEEE Trans. On Signal Processing, 39(4):806-814, April 1991.