  The ResNet-based architecture has been widely adopted to extract speaker
embeddings for text-independent speaker verification systems. By introducing
the residual connections to the CNN and standardizing the residual blocks, the
ResNet structure is capable of training deep networks to achieve highly
competitive recognition performance. However, when the input feature space
becomes more complicated, simply increasing the depth and width of the ResNet
network may not fully realize its performance potential. In this paper, we
present two extensions of the ResNet architecture, ResNeXt and Res2Net, for
speaker verification. Originally proposed for image recognition, the ResNeXt
and Res2Net introduce two more dimensions, cardinality and scale, in addition
to depth and width, to improve the model's representation capacity. By
increasing the scale dimension, the Res2Net model can represent multi-scale
features with various granularities, which particularly facilitates speaker
verification for short utterances. We evaluate our proposed systems on three
speaker verification tasks. Experiments on the VoxCeleb test set demonstrated
that the ResNeXt and Res2Net can significantly outperform the conventional
ResNet model. The Res2Net model achieved superior performance by reducing the
EER by 18.5% relative. Experiments on the other two internal test sets of
mismatched conditions further confirmed the generalization of the ResNeXt and
Res2Net architectures against noisy environment and segment length variations.
