Paper: Discriminative Pronunciation Modeling: A Large-Margin, Feature-Rich Approach

ACL ID P12-1021
Title Discriminative Pronunciation Modeling: A Large-Margin, Feature-Rich Approach
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We address the problem of learning the map- ping between words and their possible pro- nunciations in terms of sub-word units. Most previous approaches have involved genera- tive modeling of the distribution of pronuncia- tions, usually trained to maximize likelihood. We propose a discriminative, feature-rich ap- proach using large-margin learning. This ap- proach allows us to optimize an objective closely related to a discriminative task, to incorporate a large number of complex fea- tures, and still do inference efficiently. We test the approach on the task of lexical access; that is, the prediction of a word given a pho- netic transcription. In experiments on a sub- set of the Switchboard conversational speech corpus, our models thus far improve classi- fication error rates from a previ...