Attention enhanced dynamic kernel convolution for TDNN-based speaker verification

Speaker embedding is a state-of-the-art front-end module, which is used to extract discriminative speaker features for speaker-related tasks. The Time Delay Neural Network (TDNN) has been a classical network architecture since it was first applied on speaker related tasks known as X-vector. In this paper, we propose new network structures based on current popular ECAPA-TDNN. We propose a dynamic kernel convolution module to extract features from short-term and long-term context adaptively, thus achieving multi-scale receptive fields. We also apply three enhanced attention modules instead of plain Squeeze-Excitation (SE) layer to realize more efficient information interaction between channels and spaces. The proposed architectures are superior to the most advanced network, with an optimal Equal Error Rate (EER) of 6.40% and a parameters reduction of 6.32%, they also achieve better performances when speaker utterances are shortened.