A method and apparatus of modeling a word 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. In order 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. From the prior values of the weighting factors, and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of component sounds is estimated. A posterior value for the first weighting factor is estimated from the conditional probability. By constructing word models from composite elemental models, and by constructing composite elemental models from primitive elemental models, it is possible for the resulting word model to closely represent many variations in the pronunciation of a word. By providing a relatively small set of primitive elemental models in comparison to a relatively large vocabulary of words, the models can 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. <IMAGE>
Method and apparatus for modeling words with composite Markov models