  In this paper, we propose a speaker verification method by an Attentive
Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can
acquire both local spatial information and global sequential information from
the input speech recordings. In the proposed method, logarithm Mel spectrum is
extracted from each speech recording and then fed to the proposed AMCRN for
learning speaker embedding. Afterwards, the learned speaker embedding is fed to
the back-end classifier (such as cosine similarity metric) for scoring in the
testing stage. The proposed method is compared with state-of-the-art methods
for speaker verification. Experimental data are three public datasets that are
selected from two large-scale speech corpora (VoxCeleb1 and VoxCeleb2).
Experimental results show that our method exceeds baseline methods in terms of
equal error rate and minimal detection cost function, and has advantages over
most of baseline methods in terms of computational complexity and memory
requirement. In addition, our method generalizes well across truncated speech
segments with different durations, and the speaker embedding learned by the
proposed AMCRN has stronger generalization ability across two back-end
classifiers.
