Sequential Noise Compensation by Sequential Monte Carlo Method

Abstract

We present a sequential Monte Carlo method applied to additive noise compensation for robust speech recognition in time-varying noise. The method generates a set of samples according to the prior distribution given by clean speech models and noise prior evolved from previous estimation. An explicit model representing noise ef- fects on speech features is used, so that an extended Kalman filter is constructed for each sample, generating the updated continuous state estimate as the estimation of the noise parameter, and predic- tion likelihood for weighting each sample. Minimum mean square error (MMSE) inference of the time-varying noise parameter is car- ried out over these samples by fusion the estimation of samples ac- cording to their weights. A residual resampling selection step and a Metropolis-Hastings smoothing step are used to improve calcula- tion e#ciency. Experiments were conducted on speech recognition in simulated non-stationary noises, where noise power changed ar- tificially, and highly non-stationary Machinegun noise. In all the experiments carried out, we observed that the method can have sig- nificant recognition performance improvement, over that achieved by noise compensation with stationary noise assumption. 1 Introduction