  In this paper, we describe the top-scoring submissions for team RTZR VoxCeleb
Speaker Recognition Challenge 2022 (VoxSRC-22) in the closed dataset, speaker
verification Track 1. The top performed system is a fusion of 7 models, which
contains 3 different types of model architectures. We focus on training models
to learn extra-temporal information. Therefore, all models were trained with
4-6 second frames for each utterance. Also, we apply the Large Margin
Fine-tuning strategy which has shown good performance on the previous
challenges for some of our fusion models. While the evaluation process, we
apply the scoring methods with adaptive symmetric normalization (AS-Norm) and
matrix score average (MSA). Finally, we mix up models with logistic regression
to fuse all the trained models. The final submission achieves 0.165 DCF and
2.912% EER on the VoxSRC22 test set.
