Technical Program

Paper Detail

Paper:	SLP-P11.2
Session:	Front-end For Robust Speech Recognition
Time:	Wednesday, May 17, 16:30 - 18:30
Presentation:	Poster
Topic:	Speech and Spoken Language Processing: Feature-based Robust Speech Recognition (e.g., noise, etc)
Title:	Sequential Non-Stationary Noise Tracking Using Particle Filtering with Switching Dynamical System
Authors:	Masakiyo Fujimoto, Satoshi Nakamura, ATR Spoken Language Communication Research Laboratories, Japan
Abstract:	This paper addresses a speech recognition problem in non-stationary noise environments: the estimation of noise sequences. To solve this problem, we present a particle filter-based sequential noise estimation method for the front-end processing of speech recognition. In the proposed method, the particle filter is defined by a dynamical system based on Polyak averaging and feedback. We also introduce a switching dynamical system into the particle filter to cope with the state transition characteristics of non-stationary noise. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary noise environments by a noise compensation method with stationary noise assumptions.