  Speaker verification systems experience significant performance degradation
when tasked with short-duration trial recordings. To address this challenge, a
multi-scale feature fusion approach has been proposed to effectively capture
speaker characteristics from short utterances. Constrained by the model's size,
a robust backbone Enhanced Res2Net (ERes2Net) combining global and local
feature fusion demonstrates sub-optimal performance in short-duration speaker
verification. To further improve the short-duration feature extraction
capability of ERes2Net, we expand the channel dimension within each stage.
However, this modification also increases the number of model parameters and
computational complexity. To alleviate this problem, we propose an improved
ERes2NetV2 by pruning redundant structures, ultimately reducing both the model
parameters and its computational cost. A range of experiments conducted on the
VoxCeleb datasets exhibits the superiority of ERes2NetV2, which achieves EER of
0.61% for the full-duration trial, 0.98% for the 3s-duration trial, and 1.48%
for the 2s-duration trial on VoxCeleb1-O, respectively.
