Robust speech features extraction in convolutional noise environment
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摘要: 提出了一种基于独立分量分析(ICA)的语音信号鲁棒特征提取算法,用以解决在卷积噪声环境下语音信号的训练与识别特征不匹配的问题。该算法通过短时傅里叶变换将带噪语音信号从时域转换到频域后,采用复值ICA方法从带噪语音的短时谱中分离出语音信号的短时谱,然后根据所得到的语音信号短时谱计算美尔倒谱系数(MFCC)及其一阶差分作为特征参数。在仿真与真实环境下汉语数字语音识别实验中,所提算法相比较传统的MFCC其识别正确率分别提升了34.8%和32.6%。实验结果表明基于ICA方法的语音特征在卷积噪声环境下具有良好的鲁棒性。Abstract: To resolve the mismatch between training features and testing features in convolutive noise environment,a robust speech features extraction algorithm based on Independent Component Analysis (ICA) is proposed.Noisy speech signals are firstly converted from time-domain to frequency-domain via short time Fourier transform,then a complex ICA algorithm is used to acquire short-time spectrum of speech signal from that of noisy speech signal,furthermore,Mel Frequency Cepstral Coefficients (MFCC) and its 1-order differential coefficients are computed in accordance with the separated speech signals frequency spectrum.Simulation and real environment experiments on different noisy Chinese digit recognition are carried out.The results show that the recognition ratio of the proposed algorithm obtains the relative increasing of 34.8%and 32.6%compared with conventional MFCC,which reveal that the speech features based on ICA have a good robust performance.
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