Unsupervised cross-domain speaker recognition based on distribution alignment and adversarial learning

Domain mismatch has become one of the biggest challenges for realistic speaker recognition systems,especially labeled data in the target domain are unavailable.The proposed methods fuse with adversarial learning to extract speaker discriminative features.It reduces domain discrepancy by distribution alignment during the training stage.Consistent performance improvements are achieved under variety of domain mismatch circumstances.For text-dependent tasks,adversarial learning and distribution alignment work together to reduce the equal error rates 11% relatively.As for text-independent tasks,adversarial learning can hardly make contributions while our distribution alignment still achieves a relative 8% improvement.The proposed methods can steadily improve the performance effectively for unsupervised cross-domain speaker recognition.