  The majority of recent state-of-the-art speaker verification architectures
adopt multi-scale processing and frequency-channel attention mechanisms.
Convolutional layers of these models typically have a fixed kernel size, e.g.,
3 or 5. In this study, we further contribute to this line of research utilising
a selective kernel attention (SKA) mechanism. The SKA mechanism allows each
convolutional layer to adaptively select the kernel size in a data-driven
fashion. It is based on an attention mechanism which exploits both frequency
and channel domain. We first apply existing SKA module to our baseline. Then we
propose two SKA variants where the first variant is applied in front of the
ECAPA-TDNN model and the other is combined with the Res2net backbone block.
Through extensive experiments, we demonstrate that our two proposed SKA
variants consistently improves the performance and are complementary when
tested on three different evaluation protocols.
