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Abstract
Dynamic Bayesian networks (DBNs) are a class of directed graphical models for use on variable length sequences. DBNs have been applied to a number of tasks including automatic speech recognition, language processing, and DNA trace alignment. This talk will begin with a description of my recent work on reducing errors from burst noise in speech recognition using a DBN that combines a conventional phone-based speech recognizer with a classifier that detects syllable locations. The second portion of the talk will introduce several innovations for reducing the computational requirements of probabilistic inference on these types of models.
Biography
Chris Bartels is a Ph.D. candidate in the Department of Electrical Engineering at the University of Washington. He received his M.S. degree from the University of Washington in 2004 and his B.S. degree in computer engineering from the University of Kansas in 1999. Prior to his graduate studies he developed embedded software for GPS and sonar systems at GARMIN International. His research interests include graphical models in automatic speech recognition and inference in graphical models.