Minimum classification error training of hidden Markov models for sequential data in the wavelet domain

Abstract

In the last years there has been increasing interest in developing discriminative training methods for hidden Markov models, with the aim to improve their performance in classification and pattern recognition tasks. Although several advances have been made in this area, they have been targeted almost exclusively to standard models whose conditional observations are given by a Gaussian mixture density. In parallel with this development, a special kind of hidden Markov models defined in the wavelet domain has found wide-spread use in the signal and image processing community. Nevertheless, these models have been typically restricted to fully-tied parameter training using a single sequence and maximum likelihood estimates. This paper takes a step forward in the development of sequential pattern recognizers based on wavelet-domain hidden Markov models by introducing a new discriminative training method. The learning strategy relies on the minimum classification error approach and provides reestimation formulas for fully non-tied models. Numerical experiments on a simple phoneme recognition task show important improvement over the recognition rate achieved by the same models trained under the maximum likelihood estimation approach.