The knowledge of speech presence probability (SPP) plays an essential role in noise estimation and speech enhancement. Single channel SPP estimation and centralized multi-channel SPP estimation have been well studied. However, how to estimate SPP in wireless acoustic sensor networks (WASNs) remains a great challenge and few efforts can be found in this topic, particularly for WASN applications with multiple speakers. Accordingly, this paper is devoted to the problem of SPP estimation in WASNs and it presents a distributed model-based SPP estimation method for multi-speaker detection, which does not need any fusion center. A distributed k-means clustering method is first used to cluster the nodes into subnetworks, which detect different speakers. For each node in the subnetwork, the speech and noise power spectral densities are estimated locally by using a model-based method, then a distributed SPP estimator is developed and applied in every subnetwork. A distributed consensus method is used to obtain the distributed clustering and the distributed SPP estimation. Simulation results show that the proposed distributed clustering method can assign nodes into subnetworks based on their noisy observations. Moreover, the proposed distributed SPP estimator achieves robust speech detection performance under different noise conditions.

中文翻译：

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无线声学传感器网络中基于模型的分布式节点聚类和多扬声器语音存在概率估计。

语音存在概率（SPP）的知识在噪声估计和语音增强中起着至关重要的作用。已经对单通道SPP估计和集中式多通道SPP估计进行了深入研究。但是，如何估计无线声传感器网络（WASN）中的SPP仍然是一个巨大的挑战，在本主题中几乎找不到任何努力，特别是对于具有多个扬声器的WASN应用而言。因此，本文专门针对WASN中的SPP估计问题，提出了一种不需要任何融合中心的基于模型的分布式多说话者检测SPP估计方法。分布式k-means聚类方法首先用于将节点聚类为子网，以检测不同的说话者。对于子网络中的每个节点，使用基于模型的方法在本地估计语音和噪声功率谱密度，然后开发分布式SPP估计器并将其应用于每个子网络。分布式共识方法用于获得分布式聚类和分布式SPP估计。仿真结果表明，所提出的分布式聚类方法可以根据节点的嘈杂观测将节点分配到子网中。而且，所提出的分布式SPP估计器在不同噪声条件下实现了鲁棒的语音检测性能。