Improving spoken dialogue understanding using phonetic mixture models.

Title Improving spoken dialogue understanding using phonetic mixture models.
Publication TypeConference Proceedings
Year of Conference2011
AuthorsWang, W. Y., R. Artstein, A. Leuski, and D. R. Traum
Conference NameProceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference
Date PublishedMay 2011
Conference LocationPalm Beach, Florida

Augmenting word tokens with a phonetic representation, derived from a dictionary, improves the performance of a Natural Language Understanding component that interprets speech recognizer output: we observed a 5% to 7% reduction in errors across a wide range of response return rates. 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.