This report describes the UNISOUND submission for Track1 and Track2 of
VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC 2023). We submit the same
system on Track 1 and Track 2, which is trained with only VoxCeleb2-dev.
Large-scale ResNet and RepVGG architectures are developed for the challenge. We
propose a consistency-aware score calibration method, which leverages the
stability of audio voiceprints in similarity score by a Consistency Measure
Factor (CMF). CMF brings a huge performance boost in this challenge. Our final
system is a fusion of six models and achieves the first place in Track 1 and
second place in Track 2 of VoxSRC 2023. The minDCF of our submission is 0.0855
and the EER is 1.5880%.

UNISOUND 在 VoxCeleb 2023 声纹识别挑战中提出了一种基于 ResNet 和 RepVGG 架构的一致性感知分数校准方法，通过 Consistency Measure Factor（CMF）利用音频声纹的相似性分数稳定性，该方法大大提升了性能，在挑战中获得了第一名（Track 1）和第二名（Track 2）。

2023 VoxCeleb 演讲者识别挑战中的 UNISOUND 系统

UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023

Although residual connection enables training very deep neural networks, it
is not friendly for online inference due to its multi-branch topology. This
encourages many researchers to work on designing DNNs without residual
connections at inference. For example, RepVGG re-parameterizes multi-branch
topology to a VGG-like (single-branch) model when deploying, showing great
performance when the network is relatively shallow. However, RepVGG can not
transform ResNet to VGG equivalently because re-parameterizing methods can only
be applied to linear blocks and the non-linear layers (ReLU) have to be put
outside of the residual connection which results in limited representation
ability, especially for deeper networks. In this paper, we aim to remedy this
problem and propose to remove the residual connection in a vanilla ResNet
equivalently by a reserving and merging (RM) operation on ResBlock.
Specifically, the RM operation allows input feature maps to pass through the
block while reserving their information and merges all the information at the
end of each block, which can remove residual connections without changing the
original output. As a plug-in method, RM Operation basically has three
advantages: 1) its implementation makes it naturally friendly for high ratio
network pruning. 2) it helps break the depth limitation of RepVGG. 3) it leads
to better accuracy-speed trade-off network (RMNet) compared to ResNet and
RepVGG. We believe the ideology of RM Operation can inspire many insights on
model design for the community in the future. Code is available at:
this https URL

本文提出一种去除残差连接的新方法（RM 操作），可以使深层神经网络更加高效。我们将这种方法应用于 ResNet 中，可以消除残差连接而不影响原始输出。RM 操作的实现使其自然而然地适合高比率网络剪枝；它可以打破 RepVGG 的深度限制；同时比 ResNet 和 RepVGG 具有更好的精度速度权衡网络（RMNet）。我们相信 RM 操作的思想可以启发未来社区的模型设计。