Vector quantization for the efficient computation of continuous density likelihoods

In speech recognition systems based on continuous observation density hidden Markov models, the computation of the state likelihoods is an intensive task. The author presents an efficient method for the computation of the likelihoods defined by weighted sums (mixtures) of Gaussians. This method uses vector quantization of the input feature vector to identify a subset of Gaussian neighbors. It is shown that, under certain conditions, instead of computing the likelihoods of all the Gaussians, one needs to compute the likelihoods of only the Gaussian neighbours. Significant (up to a factor of nine) likelihood computation reductions have been obtained on various data bases, with only a small loss of recognition accuracy.<>