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Gaussian Model-Based Multichannel Speech Presence Probability

机译:基于高斯模型的多通道语音存在概率

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The knowledge of the target speech presence probability in a mixture of signals captured by a speech communication system is of paramount importance in several applications including reliable noise reduction algorithms. In this correspondence, we establish a new expression for speech presence probability when an array of microphones with an arbitrary geometry is used. Our study is based on the assumption of the Gaussian statistical model for all signals and involves the noise and noisy data statistics only. In comparison with the single-channel case, the new proposed multichannel approach can significantly increase the detection accuracy. In particular, when the additive noise is spatially coherent, perfect speech presence detection is theoretically possible, while when the noise is spatially white, a coherent summation of speech components is performed to allow for enhanced speech presence probability estimation.
机译:在语音通信系统捕获的信号混合中,目标语音存在概率的知识在包括可靠降噪算法在内的几种应用中至关重要。在这种对应关系中,当使用具有任意几何形状的麦克风阵列时,我们为语音存在概率建立一个新表达式。我们的研究基于所有信号的高斯统计模型的假设,并且仅涉及噪声和噪声数据统计。与单通道情况相比,新提出的多通道方法可以显着提高检测精度。特别地,当附加噪声在空间上是相干的时,理论上可以进行完美的语音存在检测,而当噪声在空间上是白时,执行语音分量的相干求和以允许增强语音存在概率估计。

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