Tongji University Team for the VoxCeleb Speaker Recognition Challenge 2020
This is an incremental improvement for speaker recognition systems in a specific competition.
The team tackled speaker recognition by developing multiple ResNet-34 variants with different loss functions and data augmentation, achieving 0.2800 DCF and 4.7770% EER on the VoxCeleb challenge.
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.