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An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

机译：具有附加噪声和信道失真的语音最长迭代迭代最长匹配段方法

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This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.
机译：本文提出了一种新的语音增强方法，该方法可从涉及噪声和通道失真（即卷积噪声）的单通道测量中进行语音增强，并展示其在鲁棒语音识别和改善嘈杂语音质量方面的应用。该方法基于从干净的宽带语音语料库中找到最长的匹配段（LMS）。该方法在我们之前的LMS研究中增加了三个新颖的发展。首先，我们解决信道失真以及附加噪声的问题。其次，我们提出了一种用于语音估计的噪声建模的改进方法。第三，我们提出了一种迭代算法，可以更新语料库数据模型的噪声和通道估计。在使用语音识别作为Aurora 4数据库测试的实验中，使用我们的增强方法作为特征提取的预处理器可以显着提高基线识别系统的性能。在与常规增强算法的另一个比较中，LMS算法的PESQ和分段SNR评级均优于其他用于增强语音噪声的方法。

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