@inbook {932,
	title = {Improving Spoken Dialogue Understanding Using Phonetic Mixture Models},
	booktitle = {Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches},
	year = {2012},
	pages = {225-238},
	chapter = {15},
	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},
	author = {William Yang Wang and Ron Artstein and Anton Leuski and David R. Traum}
}
