  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.
