| 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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