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The ResNet-based architecture has been widely adopted to extract speakerembeddings for text-independent speaker verification systems. By introducingthe residual connections to the CNN and standardizing the residual blocks, theResNet structure is capable of training deep networks to achieve highlycompetitive recognition performance. However, when the input feature spacebecomes more complicated, simply increasing the depth and width of the ResNetnetwork may not fully realize its performance potential. In this paper, wepresent two extensions of the ResNet architecture, ResNeXt and Res2Net, forspeaker verification. Originally proposed for image recognition, the ResNeXtand Res2Net introduce two more dimensions, cardinality and scale, in additionto depth and width, to improve the model's representation capacity. Byincreasing the scale dimension, the Res2Net model can represent multi-scalefeatures with various granularities, which particularly facilitates speakerverification for short utterances. We evaluate our proposed systems on threespeaker verification tasks. Experiments on the VoxCeleb test set demonstratedthat the ResNeXt and Res2Net can significantly outperform the conventionalResNet model. The Res2Net model achieved superior performance by reducing theEER by 18.5% relative. Experiments on the other two internal test sets ofmismatched conditions further confirmed the generalization of the ResNeXt andRes2Net architectures against noisy environment and segment length variations.

ResNeXt and Res2Net Structures for Speaker Verification