Development of Noise Robust Speech Recognition and Its Application on Mobile Environment

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1) Noisy speech recognition using DMHMMsWe have proposed new methods of robust speech recognition using discrete-mixture HMMs (DMHMMs). The aim of this work is to develop robust speech recognition for adverse conditions that contain both stationary and non-stationary noise. In particular, we focus on the issue of impulsive noise, which is a major problem in practical speech recognition system. In order to solve the problem, we have proposed two methods. First, an estimation method of DMHMM parameters based on MAP has been proposed aiming to improve trainability. The second is a method of compensating the observation probabilities of DMHMMs by threshold to reduce adverse effect of outlier values. Experimental evaluations on Japanese LVCSR for read newspaper speech showed that the proposed method achieved the average error rate reduction of 28.1% in adverse conditions that contain both stationary and impulsive noises.2) Model Based Histogram Equalization for Noise Robust Speech Recognition by Using DMHMMsTowards further improvement of noisy speech recognition, we have proposed a novel normalization method for codebooks of DMHMMs in this paper. The codebook normalization method is based on histogram equalization (HEQ) and it can compensate the non-linear effects of additive noise in model space. The proposed method was compared with both conventional continuous-mixture HMMs (CHMMs) and DMHMMs. It showed that the proposed method obtained the best performance, and obtained an average relative improvement of 29.2% over the CHMM baseline.