Nonlinear Statistical Matching for Subband Robust Speech Recognition
非线性统计匹配用于子带鲁棒语音识别

The performance of the speech recognition systems is deteriorated dramatically under noise condition for variation of speech signal. According to the auditory tests, this paper proposes a new nonlinear sub-band Maximum A Posteriori (MAP)statistical matching algorithm based on the independent sub-band analysis. According to the perception of human's ear and noise feature of different frequency-bands, the algorithm compensates the effects of noise with statistical matching, MAP estimation and HMM/MLP nonlinear mapping. The test shows that the proposed algorithm improves the recognition performance notably under noise condition.