This report describes our speaker verification systems for the tasks of the
CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022). This challenge
includes two tasks, namely speaker verification(SV) and speaker retrieval(SR).
The SV task involves two tracks: fixed track and open track. In the fixed
track, we only used CN-Celeb.T as the training set. For the open track of the
SV task and SR task, we added our open-source audio data. The ResNet-based,
RepVGG-based, and TDNN-based architectures were developed for this challenge.
Global statistic pooling structure and MQMHA pooling structure were used to
aggregate the frame-level features across time to obtain utterance-level
representation. We adopted AM-Softmax and AAM-Softmax combined with the
Sub-Center method to classify the resulting embeddings. We also used the
Large-Margin Fine-Tuning strategy to further improve the model performance. In
the backend, Sub-Mean and AS-Norm were used. In the SV task fixed track, our
system was a fusion of five models, and two models were fused in the SV task
open track. And we used a single system in the SR task. Our approach leads to
superior performance and comes the 1st place in the open track of the SV task,
the 2nd place in the fixed track of the SV task, and the 3rd place in the SR
task.

在 CN-Celeb 讲者识别挑战赛中，我们使用 ResNet、RepVGG 和 TDNN 架构，采用全局统计汇聚结构和 MQMHA 汇聚结构对声学数据进行处理，并使用 AM-Softmax 算法对其进行分类，最终取得了 SV 任务无榜首、有榜首以及 SR 任务的三个不同奖项。