Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020

• URL: http://arxiv.org/abs/2010.10145v1
• Date: Tue, 20 Oct 2020 09:25:40 GMT
• Title: Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020
• Authors: Shufan Shen, Ran Miao, Yi Wang, Zhihua Wei
• Abstract summary: We applied the RSBU-CW module to the ResNet34 framework to improve the denoising ability of the network. We trained two variants of ResNet,used score fusion and data-augmentation methods to improve the performance of the model.
• Score: 10.836635938778684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this report, we discribe the submission of Tongji University undergraduate team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020. We applied the RSBU-CW module to the ResNet34 framework to improve the denoising ability of the network and better complete the speaker verification task in a complex environment.We trained two variants of ResNet,used score fusion and data-augmentation methods to improve the performance of the model. Our fusion of two selected systems for the CLOSE track achieves 0.2973 DCF and 4.9700\% EER on the challenge evaluation set.

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