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arxiv: 2210.05092 · v1 · pith:BVMTO6UN · submitted 2022-10-11 · cs.SD · eess.AS

The DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022

Xiaoyi Qin , Na Li , Yuke Lin , Yiwei Ding , Chao Weng , Dan Su , Ming Li This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:BVMTO6UNrecord.jsonopen to challenge →

classification cs.SD eess.AS
keywords challengesystemtrack1adaptationadoptedarcfacecross-agedku-tencent
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This paper is the system description of the DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC22). In this challenge, we focus on track1 and track3. For track1, multiple backbone networks are adopted to extract frame-level features. Since track1 focus on the cross-age scenarios, we adopt the cross-age trials and perform QMF to calibrate score. The magnitude-based quality measures achieve a large improvement. For track3, the semi-supervised domain adaptation task, the pseudo label method is adopted to make domain adaptation. Considering the noise labels in clustering, the ArcFace is replaced by Sub-center ArcFace. The final submission achieves 0.107 mDCF in task1 and 7.135% EER in task3.

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