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 TDNN. We evaluate the proposed MFA on the
VoxCeleb database and observe that the proposed framework with MFA can achieve
state-of-the-art performance while reducing parameters and computation
complexity. Further, the MFA mechanism is found to be effective for speaker
verification with short test utterances.

本研究提出了多尺度频道注意力机制（MFA），其采用新颖的双通道设计，即卷积神经网络和时延神经网络，可用于对不同尺度的讲话者进行表征，并在短语态测试语句下表现出优异的成绩，其在 VoxCeleb 数据库上表现出最先进的性能，同时减少了参数和计算复杂度。