Optimizing Deep Speaker Embeddings with a Dynamic Cross Triplet Framework

Data augmentation is recognized as a powerful technique for enhancing the robustness of speaker verification systems. However, this method can lead to discrepancies between the training and inference phases in the feature domain. A common solution is to simply combine the original and augmented data for the network optimization. Unfortunately, this approach can not fully utilize the information across different feature domains. In this paper, we introduce a novel Dynamic Cross Triplet (DCT) method, and this method initially measures intra-domain and inter-domain similarities using speaker embeddings, and then transfers them across domains to address the mismatch issue. Notably, training with DCT is both efficient and effective, making it applicable to various networks. Experimental results on the benchmark Voxceleb dataset demonstrate the superiority of the proposed DCT method.