基于聚类相干特性的高混响环境下盲说话人计数

Shahab Pasha, Jacob Donley, C. Ritz
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引用次数: 8

摘要

本文提出了使用频率域相干度平方(MSC)之间的两个临时录音的语音作为一个可靠的说话人识别特征,在高混响环境的源计数应用。所提出的源计数方法不需要知道麦克风间距,也不假设源和麦克风之间有任何相对距离。源计数是基于对短时间段语音信号的频域MSC聚类。实验表明,频率域的MSC依赖于扬声器,并且该方法成功地用于在不同混响水平和麦克风间距下获得多达六个有源扬声器的高精度源计数结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind speaker counting in highly reverberant environments by clustering coherence features
This paper proposes the use of the frequency- domain Magnitude Squared Coherence (MSC) between two ad- hoc recordings of speech as a reliable speaker discrimination feature for source counting applications in highly reverberant environments. The proposed source counting method does not require knowledge of the microphone spacing and does not assume any relative distance between the sources and the microphones. Source counting is based on clustering the frequency domain MSC of the speech signals derived from short time segments. Experiments show that the frequency domain MSC is speaker-dependent and the method was successfully used to obtain highly accurate source counting results for up to six active speakers for varying levels of reverberation and microphone spacing.
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