Abstract

This paper presents an algorithm for learning the probabilities of optional phonological rules from corpora. The algorithm is based on using a speech recognition sys- tem to discover the surface pronunciations of words in speech corpora; using an automatic system obviates expensive phonetic labeling by hand. We describe the details of our algorithm and show the probabili- ties the system has learned for ten common phonological rules which model reductions and coarticulation effects. These probabilities were derived from a corpus of 7203 sentences of read speech from the Wall Street Journal, and are shown to be a reasonably close match to probabilities from pho- netically hand-transcribed data (TIMIT). Finally, we analyze the probability differ- ences between rule use in male versus fe- male speech, and suggest that the differences are caused by differing average rates of speech.

Citations