Automatic discovery of contextual factors describing phonological variation

Abstract

In this paper we describe a method for automatically discovering subsets of contextual factors which, taken together, axe useful for predicting the realizations,or pronunciations, of English words for continuous speech recognition. A decision tree is used for organizing contextual descriptions of phonological variation. This representation enables us to categorize different realizations according to the context in which they appear in the corpus. In addition, this organization permits us to consider simplifications such as pruning and branch clustering, leading to parsimonious descriptions that better predict allophones in these contexts. We created trees to examine the working assumption that preceding phoneme and following phoneme provide important contexts, as exemplified by the use of triphones in hidden Maxkov models; our results were in general accordance with the assumption. However, we found that other contexts also play a significantrole in phoneme realizations.