Collaborative and adversarial network for text-independent speaker verification in domain adaptation

Speaker verification models have achieved good results on the single genre data. But the performance degrades when model training and testing are not in the same domain. The adversarial training method is proposed to solve this problem by minimizing domain distribution differences. However, the adversarial training ignores domain-specific information for the domain-invariant speaker representations. In this paper, an improved collaborative adversarial network for domain adaptation in speaker verification is performed. Compared to the adversarial training, a collaborative discriminator is newly incorporated that learns domain-specific information at the lower layers. Further, the projection block is added to the collaborative discriminator. It reduces the noise introduced by the collaborative discriminator. Experiments are conducted in different mismatch scenarios and using different speaker encoders. All the experimental results show that the performance of this method is better than the baseline and previous work using adversarial training.