Maximum likelihood modelling of pronunciation variation

This paper addresses the problem of generating lexical word representations that properly represent natural pronunciation variations for the purpose of improved speech recognition accuracy. In order to create a consistent framework for optimisation of automatic speech recognition systems, we present a maximum likelihood based algorithm for fully automatic data-driven modelling of pronunciation. We also propose an extension of this formulation in order to achieve optimal modelling of pronunciation {\em variations}. Since different words will not in general exhibit the same amount of pronunciation variation, the procedure allows words to be represented by a different number of baseforms. The methods improve the sub-word description of the vocabulary words, and has been shown to improve recognition performance on the DARPA Resource Management task.