A Multimodal Variational Approach to Learning and Inference in Switching State Space Models

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.

2004-deng-icasspb.pdf
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In  Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing

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