  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.
