  Effective fusion of multi-scale features is crucial for improving speaker
verification performance. While most existing methods aggregate multi-scale
features in a layer-wise manner via simple operations, such as summation or
concatenation. This paper proposes a novel architecture called Enhanced Res2Net
(ERes2Net), which incorporates both local and global feature fusion techniques
to improve the performance. The local feature fusion (LFF) fuses the features
within one single residual block to extract the local signal. The global
feature fusion (GFF) takes acoustic features of different scales as input to
aggregate global signal. To facilitate effective feature fusion in both LFF and
GFF, an attentional feature fusion module is employed in the ERes2Net
architecture, replacing summation or concatenation operations. A range of
experiments conducted on the VoxCeleb datasets demonstrate the superiority of
the ERes2Net in speaker verification. Code has been made publicly available at
https://github.com/alibaba-damo-academy/3D-Speaker.
