Rao-Blackwellized Particle Filtering for Sequential Speech Enhancement

In this paper we present a method of sequential speech enhancement, where we infer clean speech signal using a Rao-Blackwellized particle filter (RBPF), given a noise-contaminated observed signal. In contrast to Kalman filtering-based methods, we consider a non-Gaussian speech generative model that is based on the generalized auto-regressive (GAR) model. Model parameters are learned by sequential expectation maximization, incorporating the RBPF. Empirical comparison to Kalman filter, confirms the high performance of the proposed method.