Optimizing speech recognition grammars using a measure of similarity between hidden Markov models

In this paper we discuss a method of optimizing weights in a stochastic finite state grammar using a measure of similarity between hidden Markov models. We compute the similarity using an edit distance and weights that are derived from the Bhattacharyya error between pairs of Gaussian mixture models. Forward-backward procedures are used to carry out the similarity computation, and to obtain the derivatives needed in gradient descent based optimization. We apply this procedure to the problem of estimating parameters of garbage models that are often included in SRGS grammars. Experimental results indicate that the method improves the garbage models and naturally results in models that are a function of their context in the grammar.