  This report describes the systems submitted to the first and second tracks of
the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020, which ranked second
in both tracks. Three key points of the system pipeline are explored: (1)
investigating multiple CNN architectures including ResNet, Res2Net and dual
path network (DPN) to extract the x-vectors, (2) using a composite angular
margin softmax loss to train the speaker models, and (3) applying score
normalization and system fusion to boost the performance. Measured on the
VoxSRC-20 Eval set, the best submitted systems achieve an EER of $3.808\%$ and
a MinDCF of $0.1958$ in the close-condition track 1, and an EER of $3.798\%$
and a MinDCF of $0.1942$ in the open-condition track 2, respectively.
