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CLOSE—A Data-Driven Approach to Speech Separation

机译:CLOSE-一种数据驱动的语音分离方法

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This paper studies single-channel speech separation, assuming unknown, arbitrary temporal dynamics for the speech signals to be separated. A data-driven approach is described, which matches each mixed speech segment against a composite training segment to separate the underlying clean speech segments. To advance the separation accuracy, the new approach seeks and separates the longest mixed speech segments with matching composite training segments. Lengthening the mixed speech segments to match reduces the uncertainty of the constituent training segments, and hence the error of separation. For convenience, we call the new approach Composition of Longest Segments, or CLOSE. The CLOSE method includes a data-driven approach to model long-range temporal dynamics of speech signals, and a statistical approach to identify the longest mixed speech segments with matching composite training segments. Experiments are conducted on the Wall Street Journal database, for separating mixtures of two simultaneous large-vocabulary speech utterances spoken by two different speakers. The results are evaluated using various objective and subjective measures, including the challenge of large-vocabulary continuous speech recognition. It is shown that the new separation approach leads to significant improvement in all these measures.
机译:本文研究单通道语音分离,假设要分离的语音信号未知,任意时间动态。描述了一种数据驱动的方法,该方法将每个混合语音段与复合训练段进行匹配,以分离底层的干净语音段。为了提高分离精度,新方法寻找并分离了最长的混合语音段,并匹配了复合训练段。延长混合语音段以使其匹配可减少组成训练段的不确定性,从而减少分离误差。为了方便起见,我们将这种新方法称为“最长细分的组合”或“ CLOSE”。 CLOSE方法包括对语音信号的远程时间动态建模的数据驱动方法,以及用匹配的复合训练片段识别最长的混合语音片段的统计方法。在《华尔街日报》数据库上进行了实验,以分离由两个不同的说话者同时说话的两种大词汇语音的混合。使用各种客观和主观措施来评估结果,包括对大词汇量连续语音识别的挑战。结果表明,新的分离方法可显着改善所有这些措施。

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