MFA: TDNN with Multi-scale Frequency-channel Attention for Text-independent Speaker Verification with Short Utterances. (arXiv:2202.01624v1 [cs.SD])

The time delay neural network (TDNN) represents one of the state-of-the-art
of neural solutions to text-independent speaker verification. However, they
require a large number of filters to capture the speaker characteristics at any
local frequency region. In addition, the performance of such systems may
degrade under short utterance scenarios. To address these issues, we propose a
multi-scale frequency-channel attention (MFA), where we characterize speakers
at different scales through a novel dual-path design which consists of a
convolutional neural network and …

arxiv attention independent mfa scale text verification