  While the use of deep neural networks has significantly boosted speaker
recognition performance, it is still challenging to separate speakers in poor
acoustic environments. To improve robustness of speaker recognition system
performance in noise, a novel two-stage attention mechanism which can be used
in existing architectures such as Time Delay Neural Networks (TDNNs) and
Convolutional Neural Networks (CNNs) is proposed. Noise is known to often mask
important information in both time and frequency domain. The proposed mechanism
allows the models to concentrate on reliable time/frequency components of the
signal. The proposed approach is evaluated using the Voxceleb1 dataset, which
aims at assessment of speaker recognition in real world situations. In addition
three types of noise at different signal-noise-ratios (SNRs) were added for
this work. The proposed mechanism is compared with three strong baselines:
X-vectors, Attentive X-vector, and Resnet-34. Results on both identification
and verification tasks show that the two-stage attention mechanism consistently
improves upon these for all noise conditions.
