  Speaker verification is to judge the similarity of two unknown voices in an
open set, where the ideal speaker embedding should be able to condense
discriminant information into a compact utterance-level representation that has
small intra-speaker distances and large inter-speaker distances.We propose a
novel model named Voice Transformer(VOT) for speaker verification. The model
consists of multiple parallel Transformers, and the outputs of these
Transformers are adaptively combined. Deeply-Fused Semantic Memory
Network(DFSMN)is integrated into the attention parts of these Transformers to
capture long-distance information and enhance the local dependencies.
Statistical pooling layers are incorporated to enhance overall performance
without significantly increasing the number of parameters. We propose a new
loss function called Additive Angular Margin Focal Loss(AAMF) to address the
hard sample mining issue.We evaluate the proposed approach on the VoxCeleb1 and
CN-Celeb2 datasets. The experimental results demonstrate that VOT achieves
state-of-the-art results, outperforming nearly all existing models. The code is
available on GitHub.
