  State-of-the-art speaker verification frameworks have typically focused on
developing models with increasingly deeper (more layers) and wider (number of
channels) models to improve their verification performance. Instead, this paper
proposes an approach to increase the model resolution capability using
attention-based dynamic kernels in a convolutional neural network to adapt the
model parameters to be feature-conditioned. The attention weights on the
kernels are further distilled by channel attention and multi-layer feature
aggregation to learn global features from speech. This approach provides an
efficient solution to improving representation capacity with lower data
resources. This is due to the self-adaptation to inputs of the structures of
the model parameters. The proposed dynamic convolutional model achieved 1.62\%
EER and 0.18 miniDCF on the VoxCeleb1 test set and has a 17\% relative
improvement compared to the ECAPA-TDNN using the same training resources.
