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

Robust and far-field speech recognition is critical to enable true hands-free
communication. In far-field conditions, signals are attenuated due to distance.
To improve robustness to loudness variation, we introduce a novel frontend
called per-channel energy normalization (PCEN). The key ingredient of PCEN is
the use of an automatic gain control based dynamic compression to replace the
widely used static (such as log or root) compression. We evaluate PCEN on the
keyword spotting task. On our large rerecorded noisy and far-field eval sets,
we show that PCEN significantly improves recognition performance. Furthermore,
we model PCEN as neural network layers and optimize high-dimensional PCEN
parameters jointly with the keyword spotting acoustic model. The trained PCEN
frontend demonstrates significant further improvements without increasing model
complexity or inference-time cost.

该研究介绍了一种新的前端 Technique 叫作 PCEN，它使用基于自动增益控制的动态压缩来替代广泛使用的静态压缩以提高在远场条件下的语音识别性能并进一步优化具有高维参数的 PCEN，其在关键字检测任务中的表现显示了显著的性能提升。