In this technical report, we describe the Royalflush submissions for the
VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our submissions
contain track 1, which is for supervised speaker verification and track 3,
which is for semi-supervised speaker verification. For track 1, we develop a
powerful U-Net-based speaker embedding extractor with a symmetric architecture.
The proposed system achieves 2.06% in EER and 0.1293 in MinDCF on the
validation set. Compared with the state-of-the-art ECAPA-TDNN, it obtains a
relative improvement of 20.7% in EER and 22.70% in MinDCF. For track 3, we
employ the joint training of source domain supervision and target domain
self-supervision to get a speaker embedding extractor. The subsequent
clustering process can obtain target domain pseudo-speaker labels. We adapt the
speaker embedding extractor using all source and target domain data in a
supervised manner, where it can fully leverage both domain information.
Moreover, clustering and supervised domain adaptation can be repeated until the
performance converges on the validation set. Our final submission is a fusion
of 10 models and achieves 7.75% EER and 0.3517 MinDCF on the validation set.

本文介绍了在 VoxCeleb2022 挑战中的 Royalflush 提交结果。我们提出了针对监督和半监督说话人验证的强大 U-Net 架构的说话人嵌入提取器，最终得到了 10 个模型的融合结果，取得了较好的 VoxSRC-22 挑战的性能。