Speech recognition using continuous density hidden markov models

Details Speech recognition using continuous density hidden markov models Speech recognition using continuous density hidden markov models

1993-03-12

1996-04-09

MacDonald, Allen R.

395 213, G10L 900

Patent

active

055069339

ABSTRACT:
A recognition system comprises a feature extractor for extracting a feature vector x from an input speech signal, and a recognizing section for defining continuous density Hidden Markov Models of predetermined categories k as transition network models each having parameters of transition probabilities p(k,i,j) that a state Si transits to a next state Sj and output probabilities g(k,s) that a feature vector x is output in transition from the state Si to one of the states Si and Sj, and recognizing the input signal on the basis of similarity between a sequence X of feature vectors extracted by the feature extractor and the continuous density HMMs. Particularly, the recognizing section includes a memory section for storing a set of orthogonal vectors .phi..sub.m (k,s) provided for the continuous density HMMs, and a modified CDHMM processor for obtaining each of the output probabilities g(k,s) for the continuous density HMMs in accordance with corresponding orthogonal vectors .phi..sub.m (k,s).

REFERENCES:
patent: 4624011 (1986-11-01), Watanabe et al.
C. S. Chen, K.-S. Huo, "Karhunen-Loeve Transformation for Data Compression and Speech Synsnesis," IEEE Proceedings, v. 138, i.5, pp. 377-380, Oct. 1991.
A. Papoulis, Probability, Random Varnables, and Stockastic Processes, McGraw-Hill, N.Y., N.Y., 1984, p. 179.
Parosns, Voice and Speech Processing, McGraw Hill, New York, NY (1987), pp. 182-185.

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