Technical Program

Paper Detail

Paper:MLSP-L3.2
Session:Learning Theory and Modeling
Time:Friday, May 21, 15:50 - 16:10
Presentation: Lecture
Topic: Machine Learning for Signal Processing: Bayesian Learning and Modeling
Title: A MULTIMODAL VARIATIONAL APPROACH TO LEARNING AND INFERENCE IN SWITCHING STATE SPACE MODELS
Authors: Leo Lee; University of Waterloo, Canada / Microsoft Corporation 
 Hagai Attias; Microsoft Corporation 
 Li Deng; Microsoft Corporation 
 Paul Fieguth; University of Waterloo 
Abstract: An important general model for discrete-time signal processing is the switching state space (SSS) model, which generalizes the hidden Markov model and the Gaussian state space model. Inference and parameter estimation in this model are known to be computationally intractable. This paper presents a powerful new approximation to the SSS model. The approximation is based on a variational technique that preserves the multimodal nature of the continuous state posterior distribution. Furthermore, by incorporating a windowing technique, the resulting EM algorithm has complexity that is just linear in the length of the time series. An alternative Viterbi decoding with frame-based likelihood is also presented which is crucial for the speech application that originally motivates this work. Our experiments focus on demonstrating the effectiveness of the algorithm by extensive simulations. A typical example in speech processing is also included to show the potential of this approach for practical applications.
 
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