Dynamic Kernels and Channel Attention with Multi-Layer Embedding Aggregation for Speaker Verification. (arXiv:2211.02000v1 [cs.SD])

State-of-the-art speaker verification frameworks have typically focused on
speech enhancement techniques 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 …

aggregation arxiv attention embedding verification