The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description
Not on the map. Why?
Abstract (arXiv)
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%.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Aug 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 4.
4 papers.
- 2024ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf modelscited by 22
- 2024The VoxCeleb Speaker Recognition Challenge: A Retrospectivecited by 9
- 2024Character-aware audio-visual subtitling in contextcited by 2
- 2024To what extent can ASV systems naturally defend against spoofing attacks?cited by 1
- 2024Character-aware audio-visual subtitling in context14 Oct 2024
- 2024The VoxCeleb Speaker Recognition Challenge: A Retrospective27 Aug 2024
- 2024To what extent can ASV systems naturally defend against spoofing attacks?8 Jun 2024
- 2024ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models30 Jan 2024