Unified techniques for vector quantization and hidden Markov modeling using semi-continuous models

A semicontinuous hidden Markov model (HMM), which can be considered as a special form of continuous-mixture HMM with the continuous output probability density functions sharing in a mixture Gaussian density codebook, is proposed. The semicontinuous output probability density function is represented by a combination of the discrete output probabilities of the model and the continuous Gaussian density functions of a mixture Gaussian density codebook. The amount of training data required, as well as the computational complexity of the semicontinuous HMM, can be reduced in comparison to the continuous-mixture HMM. Parameters of the codebook and HMM can be mutually optimized to achieve an optimal model/codebook combination, which leads to a unified modeling approach to vector quantization and hidden Markov modeling of speech signals. Experimental results are included which show that the recognition accuracy of the semicontinuous HMM is measurably higher than those of both the discrete and the continuous HMM