  Deep convolutional neural networks (CNNs) have been applied to extracting
speaker embeddings with significant success in speaker verification.
Incorporating the attention mechanism has shown to be effective in improving
the model performance. This paper presents an efficient two-dimensional
convolution-based attention module, namely C2D-Att. The interaction between the
convolution channel and frequency is involved in the attention calculation by
lightweight convolution layers. This requires only a small number of
parameters. Fine-grained attention weights are produced to represent channel
and frequency-specific information. The weights are imposed on the input
features to improve the representation ability for speaker modeling. The
C2D-Att is integrated into a modified version of ResNet for speaker embedding
extraction. Experiments are conducted on VoxCeleb datasets. The results show
that C2DAtt is effective in generating discriminative attention maps and
outperforms other attention methods. The proposed model shows robust
performance with different scales of model size and achieves state-of-the-art
results.
