Title | Improving Spoken Dialogue Understanding Using Phonetic Mixture Models |
Publication Type | Book Chapter |
Year of Publication | 2012 |
Authors | Wang, W. Y., R. Artstein, A. Leuski, and D. R. Traum |
Book Title | Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches |
Chapter | 15 |
Pagination | 225-238 |
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. |
URL | http://www.igi-global.com/chapter/improving-spoken-dialogue-understanding-using/64590 |