On subband-based blind separation for noisy speech recognition
On subband-based blind separation for noisy speech recognition
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基于子带的噪声语音识别盲分离
DOI：
10.1109/iconip.1999.843987
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Te
中科院分区：
文献类型：
--
作者：
Hyung;H. Jung;Soo;Te
提出了一种在特征提取过程中对噪声语音信号进行去噪的鲁棒语音识别方法。该方法采用独立分量分析，将噪声信号从两个有噪声的语音录音中线性分离出来。此外，对该方法进行了优化，计算了一个改进的波段，该波段将FFT点值在一个波段的多个分割范围内相加，并使用求和值计算每个波段的能量。因此，解混网络的数量减少了。对于语音和噪声的瞬时混合，该方法具有与纯净语音信号相同的识别性能。对于真实环境中记录的有噪声的语音信号，分离后识别率显著提高，在信噪比很低的情况下，该方法尤其有效。
A method for denoising noisy speech signals in the feature extraction process for robust speech recognition is proposed. The method uses independent component analysis, in which a noise signal is linearly separated from two noisy speech microphone recordings. In addition, the method is optimized by computing a modified band that sums up FFT point values in several divided ranges of one band, and computes each band energy using the summed values. Thus, the number of unmixing networks is reduced. For instantaneous mixtures of speech and noise, the method showed the same recognition performance as for the clean speech signal case. For noisy speech signals recorded in real environments, the recognition rate was considerably increased after separation and the methods was particularly effective for a very low signal to noise ratio.