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 .