Pattern representation model training apparatus

Details Pattern representation model training apparatus Pattern representation model training apparatus

1991-03-21

1994-02-22

Fleming, Michael R.

G10L 902

Patent

active

052895629

ABSTRACT:
Disclosed is an Hidden Markov Model (HMM) training apparatus in which a capacity for discriminating between models is taken into consideration so as to allow a high level of recognition accuracy to be obtained. A probability of a vector sequence appearing from HMMs is computed with respect to an input vector and continuous mixture density HMMs. Through this computation, the nearest different-category HMM, with which the maximum probability is obtained and which belongs to a category different from that of a training vector sequence of a known category, is selected. The respective central vectors of continuous densities constituting the output probability densities of the same-category HMM belonging to the same category as that of the training vector sequence and the nearest different-category HMM are moved on the basis of the vector sequence.

REFERENCES:
patent: 4783804 (1988-11-01), Juang et al.
Frederick Jelinek, "Continuous Speech Recognition by Statistical Methods", Proceedings of the IEEE, vol. 64, No. 4, Apr. 1976, pp. 532-556.
L. R. Rabiner et al., "Some Properties of Continuous Hidden Markov Model Representations", AT&T Technical Journal, vol. 64, No. 6, Jul.-Aug. 1985, pp. 1251-1270.
L. R. Bahl et al., "Speech Recognition with Continuous-Parameter Hidden Markov Models,", 1988 IEEE, pp. 40-43.
L. R. Rabiner et al, "Recognition of Isolated Digits Using Hidden Markov Models with Continuous Mixture," AT&T Technical Journal, vol. 64, No. 6, 1985, pp. 1211-1234.
L. R. Bahl et al, "A New Algorithm for the Estimation of Hidden Markov Model Parameters," Proc. IEEE ICASSP 88, pp. 493-496.
Seiichi Nakagawa, "Speech Regcognition by Probability Models," The Institute of Electornics, Information and Communication Engineers (Jul. 1, 1985), pp. 26-28, 33-42, 44-46, 55-61.

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