Combinations of adaptive vector quantization methods and continuous density hidden Markov models

Mikko Kurimo and Kari Torkkola

This research topic covered testing of different variations of continuous observation density hidden Markov models (CDHMMs) as phoneme models in transcribing speech into phoneme sequences. The continuous probability density functions of observated feature vectors in each state were approximated by mixtures of simple parametric distributions such as Gaussian density functions. Due to the variability of phonemes in different situations and contexts it is necessary that the mixtures have many distribution components which are linearly combinated by coefficients representing their relative importance. To make the large distribution mixtures identifiable and tractable certain generalizations were made with the parametric distributions. The use diagonal and even identical covariance matrixes for each distribution is justified because in that way the size of the mixtures can be increased without an excessive cost.

Problems were encountered in the training of the mixtures of many continuous parametric distributions and the traditionally used Baum-Welch parameter estimation procedure seemed insufficient. The importance of the initialization of models grows as the number of distribution components is increased. Especially the initial positioning of the distributions into the feature vector space of each state becomes very difficult. This problem was solved by clustering the training samples by Self-Organizing Maps (SOMs) which increased significantly the speed of convergence in training and the final recognition accuracy in phoneme recognition experiments. When the maps were tuned by Learning Vector Quantization (LVQ) to enchance the discriminatory power of the phoneme models even lower recognition error rates were observed.