【摘要】 由于语音信号的多变性,识别系统的性能极易受噪声环境的影响而导致性能下降。该文以听觉试验为基础,提出一种新的非线性独立子带隐马尔可夫模型(HMM)最大后验统计匹配算法。该算法依据人耳感知的频选性, 根据各子带噪声特点采用统计匹配、MAP估计和HMM／MLP非线性映射来补偿噪声环境的影响。实验表明该算法明显改善了识别系统在噪声环境下的性能。更多还原

【Abstract】 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.更多还原