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.

本文介绍了 SpeakIn 团队提交的 Far-Field Speaker Verification Challenge 2022（FFSVC2022）任务一和任务二的讲话者验证（SV）系统。我们采用基于 ResNet 和 RepVGG 的架构，采用全局统计汇集结构和 MQMHA 池化结构进行特征汇集，创新性地提出了分阶段迁移学习方法来提高模型性能，解决了领域不匹配问题，并在两个挑战任务中表现出优异的性能。

2022 远场说话人验证挑战赛的 SpeakIn 说话人验证系统

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

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。