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 noisecontaminated observed signal. In contrast to Kalman filteringbased 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.

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Sunho Park, Seungjin Choi
• Keywords: Clean Speech Signal, IJCNN 2006, Sequential Speech Enhancement, Speech Generative Model