On-line adaptation of the SCHMM parameters based on the segmentai quasi-bayes learning for speech recognition

IEEE Transactions on Speech and Audio Processing

Volumn 4, Issue 2, 1996, Pages 141-144

  Huo, Qiang ^a,c   Chan, Chorkin ^a,b   Lee, Chin Hui ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^c LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; MARKOV PROCESSES; MATHEMATICAL MODELS; MATRIX ALGEBRA; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; VECTORS;

QUASI-BAYES ESTIMATION; SEMICONTINUOUS HIDDEN MARKOV MODELS;

SPEECH RECOGNITION;

EID: 0030105005     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/89.486065     Document Type: Article

References (10)

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3. Q. Huo, C. Chan, and C.-H. Lee, "Bayesian learning of the parameters of discrete and tied mixture HMM's for speech recognition," in P roc.' Eurospeech-93, Berlin, Germany, 1993, pp. III-1567-1570.
4. _, "Bayesian learning of the SCHMM parameters for speech recognition," in Proc. ICASSP-94 Adelaide, Australia, 1994, pp. 1-221-224.
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9. T. Matsuoka and C.-H. Lee, "A study of on-line Bayesian adaptation for HMM-based speech recognition," in Proc. Eurospeech-93 , 1993, Berlin, Germany, pp. 815-818.
10. A. F. M. Smith and U. E. Makov, "A quasi-Bayes sequential procedure for mixtures," Royal Stat. Soc., J., Series D, vol. 40, no. 1, pp. 106-112, 1978.