Combined Bayesian and predictive techniques for rapid speaker adaptation of continuous density hidden Markov models

Computer Speech and Language

Volumn 11, Issue 3, 1997, Pages 187-206

  Ahadi, S M ^a   Woodland, P C ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV PROCESSES; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PATTERN RECOGNITION SYSTEMS; REGRESSION ANALYSIS;

CONTINUOUS DENSITY HIDDEN MARKOV MODELS; REGRESSION BASED MODEL PREDICTION (RMP);

SPEECH RECOGNITION;

EID: 0031177213     PISSN: 08852308     EISSN: None     Source Type: Journal    
DOI: 10.1006/csla.1997.0031     Document Type: Article

References (20)

1. Ahadi, S. M. (1996). Bayesian and Predictive Techniques for Speaker Adaptation. Ph.D. Thesis, Cambridge University Engineering Department.
2. Ahadi, S. M. & Woodland, P. C. (1995). Rapid speaker adaptation using model prediction. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 1. Detroit, pp. 684-687.
3. Chatterjee, S. & Price, B. (1991). Regression Analysis by Example, 2nd edn. John Wiley and Sons, New York.
4. Cox, S. J. (1992). Speaker adaptation in speech recognition using linear regression techniques. Electronic Letters 28(22), 2093-2094.
5. Cox, S. J. (1993). Speaker adaptation using a predictive model. Proceedings of Eurospeech, Vol. 3, Berlin, pp. 2283-2286.
6. Cox, S. J. (1995). Predictive speaker adaptation in speech recognition. Computer Speech and Language 9, 1-17.
7. Duda, R. O. & Hart, P. E. (1973). Pattern Classification and Scene Analysis. John Wiley and Sons, New York.
8. Dunn, O. J. & Clark, V. A. (1987). Applied Statistics: Analysis of Variance and Regression. John Wiley and Sons, New York.
9. Furui, S. (1980). A training procedure for isolated word recognition systems. IEEE Transactions on Acoustics, Speech and Signal Processing ASSP-28(2), 129-136.
10. Gauvain, J.-L. & Lee, C.-H. (1992). Bayesian learning for hidden Markov model with Gaussian mixture observation densities. Speech Communication 11, 205-213.
11. Gauvain, J.-L. & Lee, C. H. (1994). Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing SAP-2(2), 291-298.
12. Huang, X., Alleva, F., Hon, H.-W., Hwang, M.-Y., Lee, K.-F. & Rosenfeld, R. (1993). The SPHINX-II speech recognition system: an overview. Computer Speech and Language 7(2), 137-148.
13. Huo, Q. & Chan, C. (1992). Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition. Technical Report, Department of Computer Science, University of Hong Kong.
14. Lee, K.-F. (1989). Automatic Speech Recognition: The Development of the SPHINX System. Kluwer Academic Publishers, Boston.
15. Lee, C.-H. & Gauvain, J.-L. (1992). A study on speaker adaptation for continuous speech recognition. Proceedings of the ARPA Continuous Speech Recognition Workshop. Stanford, pp. 59-64.
16. Lee, C.-H., Lin, C.-H. & Juang, B.-H. (1991). A study on speaker adaptation of the parameters of continuous density hidden Markov models. IEEE Transactions on Signal Processing SP-39(4), 806-814.
17. Leggetter, C. J. & Woodland, P. C. (1995). Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer Speech & Language 9, 171-185.
18. Lindley, D. V. (1947). Regression lines and the linear functional relationship. Journal of the Royal Statistical Society B-9, 218-244.
19. Stern, R. M. & Lasry, M. J. (1987). Dynamic speaker adaptation for feature-based isolated word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing ASSP-35(6), 751-762.
20. Woodland, P. C., Odell, J. J., Valtchev, V. & Young, S. J. (1994). Large vocabulary continuous speech recognition using HTK. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. II. Adelaide, pp. 125-128.