非线性统计匹配用于子带鲁棒语音识别

孙暐; 吴镇扬; 刘海滨

非线性统计匹配用于子带鲁棒语音识别

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出版历程
- 收稿日期: 2004-08-05
- 修回日期: 2005-04-21
- 刊出日期: 2006-03-19

Nonlinear Statistical Matching for Subband Robust Speech Recognition

摘要

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。
- 语音识别;隐马尔可夫模型;最大后验估计;听觉场景分析
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 humans 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.

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参考文献(1)

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