LVQ and SOMs in training continuous and semicontinuous density hidden Markov models in phonemic decoding

Mikko Kurimo and Kari Torkkola

This research topic covered testing of Learning Vector Quantization algorithms and Self-Organizing Maps to determine good initial values for continuous (and semicontinuous) observation density hidden Markov models (CDHMMs and SCHMMs). The framework of experiments is transcribing speech into phoneme sequences using HMMs as phoneme models.

The probability density functions for the cepstral feature vectors produced in each state of each model are modeled by mixtures of multivariate Gaussian density functions. In CDHMMs each state has an unique set of parameters. In SCHMMs the mixture coefficients are unique for each state, but the collection of Gaussian densities is tied between states in order to achieve more efficient models with fewer parameters.

The mean vectors of the Gaussian densities are chosen by clustering the feature vectors of the training samples by using the Self-Organizing Maps. Then the Gaussians are modified to correspond better to the Bayesian decision surfaces between phonemes by tuning the mean vectors by LVQ. By adaptively tuning the distributions by LVQ we aim to enhance the discriminatory power of the phoneme models. To recognize observed patterns correctly, the differentiation between models is more important than likely estimates for observation probabilities.

Tests were carried out with both Baum-Welch and Viterbi training combined with different versions of the LVQ algorithm for variable codebook sizes to determine the optimal training scheme in the sense of recognition accuracy. Results of training CDHMMs by SOMs combined with Baum-Welch and LVQ combined with Viterbi training when using three male Finnish speakers were significantly better than for example training by Kmeans combined with either Baum-Welch or Viterbi training. The preliminary experiments with LVQ for the SCHMMs have also been made and the results are promising.