  This report describes the SJTU-AISPEECH system for the Voxceleb Speaker
Recognition Challenge 2022. For track1, we implemented two kinds of systems,
the online system and the offline system. Different ResNet-based backbones and
loss functions are explored. Our final fusion system achieved 3rd place in
track1. For track3, we implemented statistic adaptation and jointly training
based domain adaptation. In the jointly training based domain adaptation, we
jointly trained the source and target domain dataset with different training
objectives to do the domain adaptation. We explored two different training
objectives for target domain data, self-supervised learning based angular
proto-typical loss and semi-supervised learning based classification loss with
estimated pseudo labels. Besides, we used the dynamic loss-gate and label
correction (DLG-LC) strategy to improve the quality of pseudo labels when the
target domain objective is a classification loss. Our final fusion system
achieved 4th place (very close to 3rd place, relatively less than 1%) in
track3.
