Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker verification tasks. In this paper, we propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment (PGMVG clustering). To fully exploit the relationships among utterances and the complementarity among multiple models, our method constructs multiple k-nearest neighbors graphs based on diverse models and generates high-confidence edges using a voting mechanism. Further, to maximize the intra-class diversity, the connected subgraph is utilized to obtain the initial pseudo-labels. Finally, to prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm to decide whether merging sub-classes.

翻译：利用伪标签聚类算法从目标域大规模无标签数据中学习，是解决说话人验证任务中域自适应问题的重要方法。本文提出一种基于多模型投票与双高斯评估的渐进式子图聚类算法（PGMVG聚类）。为充分挖掘话语间的关联性及多模型间的互补性，该方法基于不同模型构建多个k近邻图，并通过投票机制生成高置信边。进一步地，为最大化类内多样性，利用连通子图获取初始伪标签。最后，为防止灾难性聚类结果，采用逐步增大k值的迭代策略，并引入基于双高斯评估的算法判断是否合并子类。