摘 要： 为提高语音识别系统在复杂声学场景下的识别率，出现了以单通道语音增强（Monaural Speech Enhancement）技术作为前端处理的鲁棒语音识别系统。尽管现有的单通道语音增强技术能够提高混响干扰下的识别率，却未能显著提升宽带非平稳噪声干扰下的系统识别率。为此，本文提出基于听觉掩蔽生成对抗网络的单通道增强方法，通过听觉掩蔽增强模型和判别器构成的对抗过程，来使增强后的语音特征满足目标语音的概率分布。实验结果表明，就语音识别率而言，所提出的听觉掩蔽生成对抗网络超越了现有的增强方法，能够相对减少19.50%的词错误率，显著提升语音识别系统的噪声鲁棒性。
　　关键词： 听觉掩蔽;生成对抗网络;单通道语音增强;鲁棒语音识别
　　文章编号： 2095-2163（2021）03-0209-06 中图分类号：TP183 文献标志码：A
　　【Abstract】To improve the accuracy of speech recognition system in the complex acoustic scene， monaural speech enhancement method is involved into the robust automatic speech recognition （ASR） system as a front-end processing. Although monaural speech enhancement has improved the recognition performance under the reverberant conditions， it failed to improve the accuracy of speeches interrupted by the wide-band non-stationary noises. To overcome this problem， the paper proposes the adversarial generative network based on auditory masking for monaural speech enhancement. Through the adversarial process between a discriminator and a masking-based enhancement model， the proposed method can make the enhanced speech features follow the distribution of target speeches. Experimental results show that，