Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks. In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC), specifically designed for semi-supervised domain adaptation. Firstly, we utilize limited labeled data from the target domain to derive domain-specific descriptors based on multiple distinct objectives, namely within-graph denoising, intra-class denoising and inter-class denoising. Then, the Infomap algorithm is adopted for embedding clustering, and the descriptors are leveraged to further refine the target domain's pseudo-labels. Moreover, to further improve the quality of pseudo labels, we introduce the subcenter-purification and progressive-merging strategy for label denoising. Our proposed MoPC method achieves 4.95% EER and ranked the 1$^{st}$ place on the evaluation set of VoxSRC 2023 track 3. We also conduct additional experiments on the FFSVC dataset and yield promising results.

翻译：利用大规模无标签数据的伪标签算法，在说话人验证任务的半监督域适应中变得至关重要。本文提出了一种名为多目标渐进式聚类（MoPC）的新型伪标签方法，专门用于半监督域适应。首先，我们利用目标域有限的标签数据，基于多个不同目标——即图内去噪、类内去噪和类间去噪——推导出域特定描述符。然后，采用Infomap算法进行嵌入聚类，并利用这些描述符进一步优化目标域的伪标签。此外，为了进一步提升伪标签质量，我们引入了子中心纯化和渐进式合并策略进行标签去噪。所提出的MoPC方法在VoxSRC 2023赛道3的评估集上实现了4.95%的等错误率（EER），并排名第一。我们还额外在FFSVC数据集上进行了实验，并取得了令人满意的结果。