基于频域ICA的语音特征增强*

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振动与冲击 ›› 2011, Vol. 30 ›› Issue (2) : 238-242.

论文

基于频域ICA的语音特征增强*

吕钊1，2 ;吴小培1; 李密2

作者信息 +

Speech Features Enhancement Based on Frequency-domain ICA

LV Zhao1,2;WU Xiao-pei1; LI Mi2

Author information +

文章历史 +

摘要

为了降低卷积噪声对语音特征所产生的影响，提高语音识别正确率，文章提出了一种基于频域ICA（Independent Component Analysis，独立分量分析）的语音特征增强算法。该算法首先使用频域ICA方法作对噪声进行估计，然后在倒谱域内将带噪语音信号的短时谱减去所估计噪声的短时谱，最后根据去噪后语音信号的短时谱计算美尔倒谱系数（MFCC）作为特征参数。在仿真和真实环境下的语音识别实验中，本文所提出的语音特征参数相比较传统的MFCC其识别正确率分别提升了38.2%和35.8%。实验结果表明本文所提算法能够较好地解决卷积噪声环境下训练与识别特征不匹配的问题，有效提高了语音识别系统的识别正确率。
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Abstract

To suppress the interference of convolutive noise on speech features and improve the rate of speech recognition, a speech features enhancement algorithm based on frequency-domain ICA (Independent Component Analysis) is presented in this paper. In the proposed algorithm, noise short-time spectrum is estimated by the frequency-domain ICA algorithm, and then noise reduction is achieved by subtracting the estimated noise short-time spectrum from the noisy speech short-time spectrum to be enhanced in the Mel-scale filter bank domain. As a result, robust MFCC (Mel Frequency Cepstral Coefficients) are acquired. Simulation and real environment experiential results reveal that the recognition ratio of the proposed algorithm obtains the relative increasing of 38.2% and 35.8% compared with conventional MFCC, which reveal that the mismatch between training features and testing features in convolutive noise environment can be suppressed effectively.