  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.
