Broad phonetic class recognition in a Hidden Markov model framework using extended Baum-Welch transformations

Abstract

In many pattern recognition tasks, given some input data and a model, a probabilistic likelihood score is often computed to measure how well the model describes the data. Extended Baum-Welch (EBW) transformations are most commonly used as a discriminative technique for estimating parameters of Gaussian mixtures, though recently they have been used to derive a gradient steepness measurement to evaluate the quality of the model to match the distribution of the data. In this paper, we explore applying the EBW gradient steepness metric in the context of Hidden Markov Models (HMMs) for recognition of broad phonetic classes and present a detailed analysis and results on the use of this gradient metric on the TIMIT corpus. We find that our gradient metric is able to outperform the baseline likelihood method, and offers improvements in noisy conditions.