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arXiv · 2209.11266

The Microsoft System for VoxCeleb Speaker Recognition Challenge 2022

Gang Liu·Tianyan Zhou·Yong Zhao·Yu Wu·Zhuo Chen·Yao Qian·Jian Wu
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Abstract

In this report, we describe our submitted system for track 2 of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). We fuse a variety of good-performing models ranging from supervised models to self-supervised learning(SSL) pre-trained models. The supervised models, trained using VoxCeleb-2 dev data, consist of ECAPA-TDNN and Res2Net in a very deep structure. The SSL pre-trained models, wav2vec and wavLM, are trained using large scale unlabeled speech data up to million hours. These models are cascaded with ECAPA-TDNN and further fine-tuned in a supervised fashion to extract the speaker representations. All 13 models are applied with score normalization and calibration and then fused into the the submitted system. We also explore the audio quality measures in the calibration stage such as duration, SNR, T60, and MOS. The best submitted system achieves 0.073 in minDCF and 1.436% in EER on the VoxSRC-22 evaluation set.

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Gang Liu, Tianyan Zhou, Yong Zhao, Yu Wu, Zhuo Chen, Yao Qian, Jian Wu. 2022-09-22. The Microsoft System for VoxCeleb Speaker Recognition Challenge 2022. https://arxiv.org/abs/2209.11266

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