  In this paper, we propose a novel bidirectional multiscale feature
aggregation (BMFA) network with attentional fusion modules for text-independent
speaker verification. The feature maps from different stages of the backbone
network are iteratively combined and refined in both a bottom-up and top-down
manner. Furthermore, instead of simple concatenation or element-wise addition
of feature maps from different stages, an attentional fusion module is designed
to compute the fusion weights. Experiments are conducted on the NIST SRE16 and
VoxCeleb1 datasets. The experimental results demonstrate the effectiveness of
the bidirectional aggregation strategy and show that the proposed attentional
fusion module can further improve the performance.
