Sequential Non-Stationary Noise Tracking Using Particle Filtering with Switching Dynamical System

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