@proceedings {925,
	title = { Improving spoken dialogue understanding using phonetic mixture models.},
	journal = {Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference},
	year = {2011},
	month = {May 2011},
	pages = {329-334},
	address = {Palm Beach, Florida},
	abstract = {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.
},
	url = {http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS11/paper/viewFile/2563/3049},
	author = {William Yang Wang and Ron Artstein and Anton Leuski and David R. Traum}
}
