Vector quantisation of the continuous distributions of an HMM speech recogniser based on mixtures of continuous distributions

摘要

The author reports on the use of vector quantisation techniques to encode the continuous multivariate distributions modeling the probability of occurrence of an observation within a state of the hidden Markov model (HMM). Standard vector quantisation of the spectral features vectors and a novel vector quantisation approach based on the distribution-free goodness-of-fit methodology are used to obtain codebooks for the representation of the probability distribution functions based on mixtures of Gaussian distributions. Initial speech recognition experiments suggest that vector quantisation techniques can be useful for representing mixtures of Gaussian distributions in HMMs.