论文标题
Tongji大学Voxceleb演讲者认可挑战赛2020
Tongji University Team for the VoxCeleb Speaker Recognition Challenge 2020
论文作者
论文摘要
在本报告中，我们描述了Tongji大学团队在Interspeech 2020年2020年Voxceleb扬声器识别挑战（VoxSRC）的近距离提交的提交。我们根据流行的Resnet-34体系结构研究了不同的说话者识别系统，并通过各种损失功能训练多个变体。引入了离线数据和在线数据增强，以改善培训集的多样性，并在后处理中应用详尽的网格搜索。我们最佳的五个近距离系统融合在挑战方面达到了0.2800 dcf和4.7770％的EER。
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.
