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This paper describes the multi-query multi-head attention (MQMHA) pooling andinter-topK penalty methods which were first proposed in our submitted systemdescription for VoxCeleb speaker recognition challenge (VoxSRC) 2021. Mostmulti-head attention pooling mechanisms either attend to the whole featurethrough multiple heads or attend to several split parts of the whole feature.Our proposed MQMHA combines both these two mechanisms and gain more diversifiedinformation. The margin-based softmax loss functions are commonly adopted toobtain discriminative speaker representations. To further enhance theinter-class discriminability, we propose a method that adds an extra inter-topKpenalty on some confused speakers. By adopting both the MQMHA and inter-topKpenalty, we achieved state-of-the-art performance in all of the public VoxCelebtest sets.

Multi-query multi-head attention pooling and Inter-topK penalty for speaker verification