Self-supervised learning has shown promising performance on speaker verification tasks, among which Self DIstillation with NO labels (DINO) is currently a widely adopted framework. As one of the unsupervised deep clustering methods, the number of valid prototypes in DINO is far less than the speakers in practical applications and remains unchanged throughout the training period, leading to severe speaker confusion and performance degradation. Therefore, a strategy named prototype division (PD) is proposed to iteratively generate fine-grained prototypes in the projection space based on the converged model to separate confused categories, where new prototypes are derived from the neighborhood of the existing valid prototypes by clustering or sampling. The results on Vox1O achieve significant improvements, relatively outperforming the baseline by 31.1% without any auxiliary loss. Further experiments on CN-Celeb also show stable improvement, proving the consistency of the proposed method.

Multi-scale context information, especially long-range dependency, has shown to be beneficial for speaker verification (SV) tasks. In this paper, we propose three methods to systematically explore long-range context SV feature extraction based on ResNet and analyze their complementarity. Firstly, the Hierarchical-split block (HS-block) is introduced to enlarge the receptive fields (RFs) and extract long-range context information over the feature maps of a single layer, where the multi-channel feature maps are split into multiple groups and then stacked together. Then, by analyzing the contribution of each location of the convolution kernel to SV, we find the traditional convolution with a square kernel is not effective for long-range feature extraction. Therefore, we propose cross convolution kernel (cross-conv), which replaces the original 3 × 3 convolution kernel with a 1 × 5 and 5 × 1 convolution kernel. Cross-conv further enlarges the RFs with the same FLOPs and parameters. Finally, the Depthwise Separable Self-Attention (DSSA) module uses an explicit sparse attention strategy to capture effective long-range dependencies globally in each channel. Experiments are conducted on the VoxCeleb and CnCeleb to verify the effectiveness and robustness of the proposed system. Experimental results show that the combination of HS-block, cross-conv, and DSSA module achieves better performance than any single method, which demonstrates the complementarity of these three methods.

Current synthetic speech detection (SSD) methods perform well on specific datasets but require improvement in interpretability and robustness. One possible reason is the lack of interpretability analysis of synthetic speech defects. In this paper, the flaws in the temporal consistency (TC) of speaker features inherent in the speech synthesis process are analyzed. Differences in the TC of intra-utterance speaker features arise due to limited control over speaker features during speech synthesis. The speech generated by text-to-speech algorithms exhibits higher TC, while the speech generated by voice conversion algorithms yeilds slightly lower TC compared to bonafide speech. Based on this finding, a new SSD method based on the TC of speaker features is proposed. Modeling the TC of intra-utterance speaker features extracted by a pre-trained ASV system can be used for SSD. The proposed method achieves equal error rates of 0.84%, 3.93%, 12.98% and 24.66% on the ASVspoof 2019 LA, 2021 LA, 2021 DF and IntheWild evaluation datasets, respectively, demonstrating strong interpretability and robustness.