Method and apparatus for modeling words with multi-arc markov models

Abstract

Modeling a word is done by concatenating a series of elemental models to form a word model. At least one elemental model in the series is a composite elemental model formed by combining the starting states of at least first and second primitive elemental models. Each primitive elemental model represents a speech component. The primitive elemental models are combined by a weighted combination of their parameters in proportion to the values of the weighting factors. To tailor the word model to closely represent variations in the pronunciation of the word, the word is uttered a plurality of times by a plurality of different speakers. Constructing word models from composite elemental models, and constructing composite elemental models from primitive elemental models enables word models to represent many variations in the pronunciation of a word. Providing a relatively small set of primitive elemental models for a relatively large vocabulary of words enables models to be trained to the voice of a new speaker by having the new speaker utter only a small subset of the words in the vocabulary.