Adaptation, Inference, and Optimization: Speech Driven Machine Learning

Speech applications (such as speech recognition) have a long history of utilizing statistical learning methodology. In this talk, we will describe how machine learning research can be motivated by the application of speech processing, including speech recognition and speech-based human-computer interaction. First, dynamic graphical models (e.g., DBNs, and CRFS) can be used to express many novel speech recognition procedures. We describe new methods to perform fast exact and approximate inference in such models. These methods involve, as expected, graph triangulation, conditioning, and search, but perhaps more surprisingly, also involve optimal bin packing, max-flow procedures, and submodular matchings. In the second part of the talk, we will describe a new speech application, the Vocal Joystick, for specifying multi-dimensional continuous control parameters using non-verbal vocalizations. This application has resulted in sample complexity bounds for model adaptation, and adaptation strategies for discriminative classifiers (SVMs and Neural Networks).

Speaker Bio

Jeff A. Bilmes is an Associate Professor in the Department of Electrical Engineering at the University of Washington, Seattle (adjunct in Linguistics and in Computer Science and Engineering). He co-founded the Signal, Speech, and Language Interpretation Laboratory at the University. He received a masters degree from MIT, and a Ph.D. in Computer Science at the University of California, Berkeley. Jeff is the main author and designer of the graphical model toolkit (GMTK), and has done much research on both structure learning of and fast probabilistic inference in dynamic Graphical models. His main research lies in statistical graphical models, speech, language and time series, machine learning, human-computer interaction, combinatorial optimization, and high-performance computing. He was a general co-chair for IEEE ASRU 2003, and HLT/NAACL 2006. He is a member of the IEEE, ACM, and ACL, is a 2001 CRA Digital-Government Research Fellow, and is a 2001 recipient of the NSF CAREER award.