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arXiv · 2308.08766

The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023

Ze Li·Yuke Lin·Xiaoyi Qin·Ning Jiang·Guoqing Zhao·Ming Li
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Abstract

This paper is the system description of the DKU-MSXF System for the track1, track2 and track3 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). For Track 1, we utilize a network structure based on ResNet for training. By constructing a cross-age QMF training set, we achieve a substantial improvement in system performance. For Track 2, we inherite the pre-trained model from Track 1 and conducte mixed training by incorporating the VoxBlink-clean dataset. In comparison to Track 1, the models incorporating VoxBlink-clean data exhibit a performance improvement by more than 10% relatively. For Track3, the semi-supervised domain adaptation task, a novel pseudo-labeling method based on triple thresholds and sub-center purification is adopted to make domain adaptation. The final submission achieves mDCF of 0.1243 in task1, mDCF of 0.1165 in Track 2 and EER of 4.952% in Track 3.

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Ze Li, Yuke Lin, Xiaoyi Qin, Ning Jiang, Guoqing Zhao, Ming Li. 2023-08-17. The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023. https://arxiv.org/abs/2308.08766

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