  In this report, we describe our submitted system for track 2 of the VoxCeleb
Speaker Recognition Challenge 2022 (VoxSRC-22). We fuse a variety of
good-performing models ranging from supervised models to self-supervised
learning(SSL) pre-trained models. The supervised models, trained using
VoxCeleb-2 dev data, consist of ECAPA-TDNN and Res2Net in a very deep
structure. The SSL pre-trained models, wav2vec and wavLM, are trained using
large scale unlabeled speech data up to million hours. These models are
cascaded with ECAPA-TDNN and further fine-tuned in a supervised fashion to
extract the speaker representations. All 13 models are applied with score
normalization and calibration and then fused into the the submitted system. We
also explore the audio quality measures in the calibration stage such as
duration, SNR, T60, and MOS. The best submitted system achieves 0.073 in minDCF
and 1.436% in EER on the VoxSRC-22 evaluation set.
