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

We describe the system used by our team for the VoxCeleb Speaker Recognition
Challenge 2022 (VoxSRC 2022) in the speaker diarization track. Our solution was
designed around a new combination of voice activity detection algorithms that
uses the strengths of several systems. We introduce a novel multi stream
approach with a decision protocol based on classifiers entropy. We called this
method a multi-stream voice activity detection and used it with standard
baseline diarization embeddings, clustering and resegmentation. With this work,
we successfully demonstrated that using a strong baseline and working only on
voice activity detection, one can achieved close to state-of-theart results.

该研究论文讨论了 VoxCeleb Speaker Recognition Challenge 2022 中讲者辨识赛道中的语音活动检测问题，提出了基于多流方法和熵决策协议的语音活动检测模型，并取得了接近最新成果的效果。

2022 Newsbridge-Telecom SudParis VoxCeleb 说话人识别挑战系统描述

The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description

This paper describes the Microsoft speaker diarization system for monaural
multi-talker recordings in the wild, evaluated at the diarization track of the
VoxCeleb Speaker Recognition Challenge(VoxSRC) 2020. We will first explain our
system design to address issues in handling real multi-talker recordings. We
then present the details of the components, which include Res2Net-based speaker
embedding extractor, conformer-based continuous speech separation with leakage
filtering, and a modified DOVER (short for Diarization Output Voting Error
Reduction) method for system fusion. We evaluate the systems with the data set
provided by VoxSRCchallenge 2020, which contains real-life multi-talker audio
collected from YouTube. Our best system achieves 3.71% and 6.23% of the
diarization error rate (DER) on development set and evaluation set,
respectively, being ranked the 1st at the diarization track of the challenge.

本文介绍了 Microsoft 公司的单声道多人对话录音扬声器辨识系统，并用 VoxCeleb 挑战赛 2020 年的说话人辨识赛道进行了评估。论文首先介绍了解决真实多人对话记录中的问题的系统设计。然后介绍了组件的细节，其中包括 Res2Net 基于说话人嵌入提取器，基于 conformer 的连续语音分离和泄漏过滤，以及修改后的 DOVER 方法用于系统融合。使用 VoxSRC challenge 2020 提供的数据集进行系统评估。我们的最佳系统在开发集上的辨识错误率（DER）为 3.71％，在评估集上的 DER 为 6.23％，并在挑战的辨识赛道上排名第一。