  Speaker verification based on ad-hoc microphone arrays has the potential of
reducing the error significantly in adverse acoustic environments. However,
existing approaches extract utterance-level speaker embeddings from each
channel of an ad-hoc microphone array, which does not consider fully the
spatial-temporal information across the devices. In this paper, we propose to
aggregate the multichannel signals of the ad-hoc microphone array at the
frame-level by exploring the cross-channel information deeply with two
attention mechanisms. The first one is a self-attention method. It consists of
a cross-frame self-attention layer and a cross-channel self-attention layer
successively, both working at the frame level. The second one learns the
cross-frame and cross-channel information via two graph attention layers.
Experimental results demonstrate that the proposed methods reach the
state-of-the-art performance. Moreover, the graph-attention method is better
than the self-attention method in most cases.
