Abstract

In this paper we describe a completely automatic algorithm that builds multiple pronunciation word models by expanding baseform pronunciations with a set of candidate phonological rules. We show how to train the probabilities of these phonological rules, and how to use these probabilities to assign pronunciation probabilities to words not seen in the training corpus. The algorithm we propose is an instance of the class of techniques we call Exploratory Computational Phonology. 1. INTRODUCTION One well-known difficulty in understanding speakerindependent continuous speech is variability in the pronunciation of words. This variability occurs across speakers and also across different contexts for a single speaker. In order to model this variation, recognition systems often use a richer lexicon in which each word has multiple pronunciations. Using a multiple-pronunciation lexicon requires setting a probability for each pronunciation. The minimal algorithm, for example, would assign each ...

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