非线性统计匹配用于子带鲁棒语音识别  

Nonlinear Statistical Matching for Subband Robust Speech Recognition

作　　者：孙暐[1] 吴镇扬[1] 刘海滨[1] 

机构地区：[1]东南大学无线电系,南京210096

出　　处：《电子与信息学报》2006年第3期480-484,共5页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(60272044)973计划(2002CB312102)资助课题

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。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.

关 键 词：语音识别 隐马尔可夫模型 最大后验估计 听觉场景分析 

分 类 号：TN391.42[电子电信—物理电子学]