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Speaker verification is to judge the similarity of two unknown voices in anopen set, where the ideal speaker embedding should be able to condensediscriminant information into a compact utterance-level representation that hassmall intra-speaker distances and large inter-speaker distances.We propose anovel model named Voice Transformer(VOT) for speaker verification. The modelconsists of multiple parallel Transformers, and the outputs of theseTransformers are adaptively combined. Deeply-Fused Semantic MemoryNetwork(DFSMN)is integrated into the attention parts of these Transformers tocapture long-distance information and enhance the local dependencies.Statistical pooling layers are incorporated to enhance overall performancewithout significantly increasing the number of parameters. We propose a newloss function called Additive Angular Margin Focal Loss(AAMF) to address thehard sample mining issue.We evaluate the proposed approach on the VoxCeleb1 andCN-Celeb2 datasets. The experimental results demonstrate that VOT achievesstate-of-the-art results, outperforming nearly all existing models. The code isavailable on GitHub.

VOT: Revolutionizing Speaker Verification with Memory and Attention Mechanisms