  Majority of the recent approaches for text-independent speaker recognition
apply attention or similar techniques for aggregation of frame-level feature
descriptors generated by a deep neural network (DNN) front-end. In this paper,
we propose methods of convolutional attention for independently modelling
temporal and frequency information in a convolutional neural network (CNN)
based front-end. Our system utilizes convolutional block attention modules
(CBAMs) [1] appropriately modified to accommodate spectrogram inputs. The
proposed CNN front-end fitted with the proposed convolutional attention modules
outperform the no-attention and spatial-CBAM baselines by a significant margin
on the VoxCeleb [2, 3] speaker verification benchmark, and our best model
achieves an equal error rate of 2:031% on the VoxCeleb1 test set, improving the
existing state of the art result by a significant margin. For a more thorough
assessment of the effects of frequency and temporal attention in real-world
conditions, we conduct ablation experiments by randomly dropping frequency bins
and temporal frames from the input spectrograms, concluding that instead of
modelling either of the entities, simultaneously modelling temporal and
frequency attention translates to better real-world performance.
