Sequential noise compensation by

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 ¯lter 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-ti¯cially, and highly non-stationary Machinegun noise. In all the experiments carried out, we observed that the method can have sig-ni¯cant recognition performance improvement, over that achieved by noise compensation with stationary noise assumption.