  In this paper, gating mechanisms are applied in deep neural network (DNN)
training for x-vector-based text-independent speaker verification. First, a
gated convolution neural network (GCNN) is employed for modeling the
frame-level embedding layers. Compared with the time-delay DNN (TDNN), the GCNN
can obtain more expressive frame-level representations through carefully
designed memory cell and gating mechanisms. Moreover, we propose a novel
gated-attention statistics pooling strategy in which the attention scores are
shared with the output gate. The gated-attention statistics pooling combines
both gating and attention mechanisms into one framework; therefore, we can
capture more useful information in the temporal pooling layer. Experiments are
carried out using the NIST SRE16 and SRE18 evaluation datasets. The results
demonstrate the effectiveness of the GCNN and show that the proposed
gated-attention statistics pooling can further improve the performance.
