Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challe

Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020

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.

同济大学VoxCeleb演讲者识别挑战赛2020年本科团队

在此报告中，我们将同济大学本科生团队提交给2020年Interspeech的2020年VoxCeleb演讲者识别挑战赛（VoxSRC）的CLOSE跟踪。我们将RSBU-CW模块应用于ResNet34框架以提高网络的去噪能力我们训练了ResNet的两种变体，使用分数融合和数据增强方法来改善模型的性能。.. 我们将两个选定的CLOSE轨道系统融合在一起，在挑战评估组上获得了0.2973 DCF和4.9700％的EER。 （阅读更多）