Learning Phonological Rule Probabilities from Speech Corpora with Exploratory Computational Phonology

Learning Phonological Rule Probabilities from Speech Corpora with Exploratory Computational Phonology Gary Tajchman, Daniel Jurafsky, and Eric Fosler International Computer Science Institute and University of California at Berkeley {tajchman,jurafsky, fosler}~icsi.berkeley.edu Abstract 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 versus fe- male speech, and suggest that the differ- ences are caused by differing average rates of speech. 1 Introduction Phonological r-ules have formed the basis of phono- logical theory for decades, although their form and their coverage of the data has changed over the years. Until recently, however, it was difficult to deter- mine the relationship between hand-written phono- logical rules and actual speech data. The current availability of large speech corpora and pronunci- ation dictionaries has allowed us to connect rules and speech in much tighter ways. For example, a number of algorithms have recently been proposed which automatically induce phonological rules from dictionaries or corpora (Gasser 1993; Ellison 1992; Daelemans c~ al. 1994). While such algorithms have successfully induced syllabicity or harmony constraints, or simple oblig- *Currently at Voice Processing Corp, 1 Main St, Cambridge, MA 02142: tajchman@vpro.eom atory phonological rules, there has been much less work on non-obligatory (optional) rules. In part this is because optional rules like flapping, vowel reduc- tion, and various coarticulation effects are postlexi- cal and often products of fast speech, and hence have been considered less central to phonological theory. In part, however, this is because optional rules are inherently probabilistic. Where obligatory rules ap- ply to every underlying form which meets the en- vironmental conditions, producing a single surface form, optional rules may not apply, and hence the underlying form may appear as the surface form, unmodified by the rule. This makes the induction problem non-deterministic, and not solvable by the above algorithms. 1 While optional rules have received less attention in linguistics because of their probabilistic nature, in speech recognition, by contrast, optional rules are commonly used to model pronunciation variation. In this paper, we employ techniques from speech recog- nition research to address the problem of assign- ing probabilities to these optional phonological rules. We introduce a completely automatic algorithm that explores the coverage of a set of phonological rules on a corpus of lexically transcribed speech using the computational resources of a speech recognition sys- tem. This algorithm belongs to …

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