Discriminative training using the trusted expectation maximization

Abstract

We present the Trusted Expectation-Maximization (TEM), a new discriminative training scheme, for speech recognition applications. In particular, the TEM algorithm may be used for Hidden Markov Models (HMMs) based discriminative training. The TEM algorithm has a form similar to the Expectation-Maximization (EM) algorithm, which is an efficient iterative procedure to perform maximum likelihood in the presence of hidden variables [1]. The TEM algorithm has been empirically shown to increase a rational objective function. In the concave regions of a rational function, it can be shown that the maximization steps of the TEM algorithm and the hypothesized EM algorithm are identical. In the TIMIT phone recognition task, preliminary experimental results show competitive optimization performance over the conventional discriminative training approaches (in terms of speech and accuracy). Copyright © 2008 ISCA.