Improving Spoken Dialogue Understanding Using Phonetic Mixture Models

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Author(s): William Yang Wang (Carnegie Mellon University, USA), Ron Artstein (Institute for Creative Technologies, University of Southern California, USA), Anton Leuski (Institute for Creative Technologies, University of Southern California, USA)and David Traum (Institute for Creative Technologies, University of Southern California, USA)
Copyright: 2012
Pages: 14
Source title: Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches
Source Author(s)/Editor(s): Chutima Boonthum-Denecke (Hampton University, USA), Philip M. McCarthy (The University of Memphis, USA)and Travis Lamkin (University of Memphis, USA)
DOI: 10.4018/978-1-61350-447-5.ch015

Keywords: Artificial Intelligence / Computational Linguistics / Computer Science & IT / Information Science Reference

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Abstract

Reasoning about sound similarities improves the performance of a Natural Language Understanding component that interprets speech recognizer output: the authors observed a 5% to 7% reduction in errors when they augmented the word strings with a phonetic representation, derived from the words by means of a dictionary. The best performance comes from mixture models incorporating both word and phone features. Since the phonetic representation is derived from a dictionary, the method can be applied easily without the need for integration with a specific speech recognizer. The method has similarities with autonomous (or bottom-up) psychological models of lexical access, where contextual information is not integrated at the stage of auditory perception but rather later.

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Improving Spoken Dialogue Understanding Using Phonetic Mixture Models

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