  With the development of deep learning, many different network architectures
have been explored in speaker verification. However, most network architectures
rely on a single deep learning architecture, and hybrid networks combining
different architectures have been little studied in ASV tasks. In this paper,
we propose the GMM-ResNext model for speaker verification. Conventional GMM
does not consider the score distribution of each frame feature over all
Gaussian components and ignores the relationship between neighboring speech
frames. So, we extract the log Gaussian probability features based on the raw
acoustic features and use ResNext-based network as the backbone to extract the
speaker embedding. GMM-ResNext combines Generative and Discriminative Models to
improve the generalization ability of deep learning models and allows one to
more easily specify meaningful priors on model parameters. A two-path
GMM-ResNext model based on two gender-related GMMs has also been proposed. The
Experimental results show that the proposed GMM-ResNext achieves relative
improvements of 48.1\% and 11.3\% in EER compared with ResNet34 and ECAPA-TDNN
on VoxCeleb1-O test set.
