The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022

• URL: http://arxiv.org/abs/2209.11625v1
• Date: Fri, 23 Sep 2022 14:51:55 GMT
• Title: The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022
• Authors: Yu Zheng, Jinghan Peng, Yihao Chen, Yajun Zhang, Jialong Wang, Min Liu, Minqiang Xu
• Abstract summary: This paper describes speaker verification systems submitted to the Far-Field Speaker Verification Challenge 2022 (FFSVC2022) The ResNet-based and RepVGG-based architectures were developed for this challenge. Our approach leads to excellent performance and ranks 1st in both challenge tasks.
• Score: 15.453882034529913
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes speaker verification (SV) systems submitted by the SpeakIn team to the Task 1 and Task 2 of the Far-Field Speaker Verification Challenge 2022 (FFSVC2022). SV tasks of the challenge focus on the problem of fully supervised far-field speaker verification (Task 1) and semi-supervised far-field speaker verification (Task 2). In Task 1, we used the VoxCeleb and FFSVC2020 datasets as train datasets. And for Task 2, we only used the VoxCeleb dataset as train set. The ResNet-based and RepVGG-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 to classify the resulting embeddings. We innovatively propose a staged transfer learning method. In the pre-training stage we reserve the speaker weights, and there are no positive samples to train them in this stage. Then we fine-tune these weights with both positive and negative samples in the second stage. Compared with the traditional transfer learning strategy, this strategy can better improve the model performance. The Sub-Mean and AS-Norm backend methods were used to solve the problem of domain mismatch. In the fusion stage, three models were fused in Task1 and two models were fused in Task2. On the FFSVC2022 leaderboard, the EER of our submission is 3.0049% and the corresponding minDCF is 0.2938 in Task1. In Task2, EER and minDCF are 6.2060% and 0.5232 respectively. Our approach leads to excellent performance and ranks 1st in both challenge tasks.

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