构建说话人识别挑战系统：来自 VoxSRC 2022 与 CNSRC 2022 的经验
Build a SRE Challenge System: Lessons from VoxSRC 2022 and CNSRC 2022
AI总结：
本文基于团队在 CNSRC 2022 和 VoxSRC 2022 中分别获得第一和第三名的经验，系统阐述了如何构建高性能说话人验证挑战系统，并对所采用的方法与流行技术进行了详细对比分析。
摘要：
许多说话人识别挑战赛已被举办，用于评估在真实场景下的说话人验证系统并探索其性能极限。基于 VoxCeleb 数据集的 Voxceleb 说话人识别挑战赛（VoxSRC）是最受欢迎的赛事。此外，今年还举办了另一个基于中文名人多体裁数据集 CN-Celeb 的挑战赛，即 CN-Celeb 说话人识别挑战赛（CNSRC）。今年，我们的团队同时参加了 CNSRC 2022 和 VoxSRC 2022 的说话人验证闭集赛道，分别获得了第一名和第三名。在大多数系统报告中，作者通常只提供其系统的描述，但缺乏对其方法的有效分析。在本文中，我们将概述如何构建一个强大的说话人验证挑战系统，并对每种方法与其他一些流行技术手段进行详细的分析比较。
英文摘要：
Many speaker recognition challenges have been held to assess the speaker verification system in the wild and probe the performance limit. Voxceleb Speaker Recognition Challenge (VoxSRC), based on the voxceleb, is the most popular. Besides, another challenge called CN-Celeb Speaker Recognition Challenge (CNSRC) is also held this year, which is based on the Chinese celebrity multi-genre dataset CN-Celeb. This year, our team participated in both speaker verification closed tracks in CNSRC 2022 and VoxSRC 2022, and achieved the 1st place and 3rd place respectively. In most system reports, the authors usually only provide a description of their systems but lack an effective analysis of their methods. In this paper, we will outline how to build a strong speaker verification challenge system and give a detailed analysis of each method compared with some other popular technical means.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
