Sequential MAP estimation based speech feature enhancement for noise robust speech recognition

In this paper, the environment mismatch due to additive noise is assumed as an additive bias in power spectral domain. It is viable to introduce some constraints on the values that the bias can take due to the internal relation between bias and noise power spectrum. We propose to introduce the noise priori knowledge into bias estimation process by using maximum a posteriori (MAP) criterion. Moreover, the mismatch is usually nonstationary in real application and sequential algorithm can be used to track time varying environment within a test utterance. This paper proposes to use the sequential techniques to estimate the bias in the MAP framework and update the parameters of noise priori adaptively. Speech recognition experiments demonstrated that the proposed algorithm outperformed sequential ML estimation method and was obviously better than the batch mode under non-stationary noise environment.