Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain
  Adaptation Speaker Verification
Zhuo Li Jingze Lu Zhenduo Zhao Wenchao Wang Pengyuan Zhang
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.