论文标题
Tongji大学本科生的Voxceleb演讲者认可挑战赛2020
Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020
论文作者
论文摘要
在本报告中，我们将Tongji大学本科团队的提交在2020年Interspeech 2020的Voxceleb扬声器识别挑战（VOXSRC）的近距离范围内。我们将RSBU-CW模块应用于resnet34框架上，以改善网络的能力，并在启动的环境中更好地完成培训的范围。提高模型性能的数据授权方法。在挑战评估集中，我们对两个封闭轨道的两个选定系统的融合达到了0.2973 DCF和4.9700 \％\％\％。
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.
