The VoxCeleb Speaker Recognition Challenge: A Retrospective
Authors: name: Jaesung Huh, name: Joon Son Chung, name: Arsha Nagrani, name: Andrew Brown, name: Jee-weon Jung, name: Daniel Garcia-Romero, name: Andrew Zisserman

The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of
challenges and workshops that ran annually from 2019 to 2023. The challenges
primarily evaluated the tasks of speaker recognition and diarisation under
various settings including: closed and open training data; as well as
supervised, self-supervised, and semi-supervised training for domain
adaptation. The challenges also provided publicly available training and
evaluation datasets for each task and setting, with new test sets released each
year. In this paper, we provide a review of these challenges that covers: what
they explored; the methods developed by the challenge participants and how
these evolved; and also the current state of the field for speaker verification
and diarisation. We chart the progress in performance over the five
installments of the challenge on a common evaluation dataset and provide a
detailed analysis of how each year's special focus affected participants'
performance. This paper is aimed both at researchers who want an overview of
the speaker recognition and diarisation field, and also at challenge organisers
who want to benefit from the successes and avoid the mistakes of the VoxSRC
challenges. We end with a discussion of the current strengths of the field and
open challenges. Project page :
https://mm.kaist.ac.kr/datasets/voxceleb/voxsrc/workshop.html