Paper: Learning Phonological Rule Probabilities From Speech Corpora With Exploratory Computational Phonology

ACL ID P95-1001
Title Learning Phonological Rule Probabilities From Speech Corpora With Exploratory Computational Phonology
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1995
Authors

This paper presents an algorithm for learn- ing the probabilities of optional phonolog- ical rules from corpora. The algorithm is based on using a speech recognition sys- tem to discover the surface pronunciations of words in spe.ech corpora; using an auto- matic 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 probabili- ties were derived from a corpus of 7203 sen- tences of read speech from the Wall Street Journal, and are shown to be a reason- ably close match to probabilities from pho- netically hand-transcribed data (TIMIT). Finally, we analyze the probability differ- ences between rule use in male v...