Tongji University Team for the VoxCeleb Speaker Recognition Challenge 2020

In this report, we describe the submission of Tongji University team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020. We investigate different speaker recognition systems based on the popular ResNet-34 architecture, and train multiple variants via v...

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Bibliographic Details
Published inarXiv.org
Main Authors Wang, Rui, Wei, Zhihua, Zhan, Yibin, Chen, Zhuoxi
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.10.2020
Subjects
Post-processing
Speech recognition
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Summary:In this report, we describe the submission of Tongji University team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020. We investigate different speaker recognition systems based on the popular ResNet-34 architecture, and train multiple variants via various loss functions. Both Offline and online data augmentation are introduced to improve the diversity of the training set, and score normalization with the exhaustive grid search is applied in the post-processing. Our best fusion of five selected systems for the CLOSE track achieves 0.2800 DCF and 4.7770% EER on the challenge.
ISSN:2331-8422