An automatic speech recognition methodology, wherein words are modeled as probabilistic networks of allophones, collects nodes in the probabilistic network into equivalence classes when those nodes have the same allophonic choices governed by the same phonological rules. The allophonic choices allow for representation of dialectic pronunciation variations between different speakers. Training data is shared among nodes in an equivalence class so that accurate pronunciation probabilities may be determined even for words for which there is only a limited amount of training data. A method is used to determine probabilities for each of a multitude of pronunciation models for each word in the vocabulary, based on automatic extraction of linguistic knowledge from sets of phonological rules, in order to robustly and accurately model dialectal variation.
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