The SpeakIn System Description for CNSRC2022

• URL: http://arxiv.org/abs/2209.10846v1
• Date: Thu, 22 Sep 2022 08:17:47 GMT
• Title: The SpeakIn System Description for CNSRC2022
• Authors: Yu Zheng, Yihao Chen, Jinghan Peng, Yajun Zhang, Min Liu, Minqiang Xu
• Abstract summary: This report describes our speaker verification systems for the tasks of the CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022) The challenge includes two tasks, namely speaker verification(SV) and speaker retrieval(SR)
• Score: 14.173172568687413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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