A semi-continuous hidden Markov model based on the multiple vector quantization codebooks is used here for large-vocabulary speaker-independent continuous speech recognition. In the techniques employed here, the semi-continuous output probability density function for each codebook is represented by a combination of the corresponding discrete output probabilities of the hidden Markov model and the continuous Gaussian density functions of each individual codebook. Parameters of vector quantization codebook and hidden Markov model are mutually optimized to achieve an optimal model/codebook combination under a unified probabilistic framework. Another advantages of this approach is the enhanced robustness of the semicontinuous output probability by the combination of multiple codewords and multiple codebooks. For a 1000-word speaker-independent continuous speech recognition using a word-pair grammar, the recognition error rate of the semi-continuous hidden Markov model was reduced by more than 29% and 41% in comparison to the discrete and continuous mixture hidden Markov model respectively.
Bibliographic reference. Huang, X. D. / Hon, Hsiao-Wuen / Lee, Kai-Fu (1989): "Large-vocabulary speaker-independent continuous speech recognition with semi-continuous hidden Markov models", In EUROSPEECH-1989, 1163-1166.