eess.AS

eess.AS - 2023-08-16

The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description

  • paper_url: http://arxiv.org/abs/2308.08294
  • repo_url: None
  • paper_authors: Nikita Torgashov, Rostislav Makarov, Ivan Yakovlev, Pavel Malov, Andrei Balykin, Anton Okhotnikov
  • for: 这篇论文是为了参加VoxCeleb Speaker Recognition Challenge 2023（VoxSRC-23）的Track 2（开放）阶段提交的。
  • methods: 这个解决方案基于深度ResNet和自动编写学习（SSL）基于模型，使用VoxCeleb2数据集和大量VoxTube数据集进行混合训练。
  • results: 最终提交在VoxSRC-23公共排名板上获得第一名，minDCF(0.05)为0.0762，EER为1.30%。
    Abstract This report describes ID R&D team submissions for Track 2 (open) to the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). Our solution is based on the fusion of deep ResNets and self-supervised learning (SSL) based models trained on a mixture of a VoxCeleb2 dataset and a large version of a VoxTube dataset. The final submission to the Track 2 achieved the first place on the VoxSRC-23 public leaderboard with a minDCF(0.05) of 0.0762 and EER of 1.30%.
    摘要 translate to Simplified Chinese:这份报告描述ID R&D团队在VoxCeleb Speaker Recognition Challenge 2023（VoxSRC-23）的跑道2（开放）中的提交。我们的解决方案基于深度ResNet和自动教育学（SSL）基于模型，在VoxCeleb2数据集和大量的VoxTube数据集的混合上进行训练。最终的提交在VoxSRC-23公共排名板上获得第一名，minDCF(0.05)为0.0762，EER为1.30%。