title: The VoxCeleb Speaker Recognition Challenge: A Retrospective
summary: 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
paper_authors._id: 66d533861ba71ac4c040eb6d
paper_authors.name: Jaesung Huh
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paper_authors.name: Joon Son Chung
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paper_authors.fullname: Joon Son Chung
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paper_authors.user: joonson
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paper_authors.name: Arsha Nagrani
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paper_authors.fullname: Arsha Nagrani 
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paper_authors.name: Andrew Brown
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paper_authors.name: Jee-weon Jung
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paper_authors.fullname: Jee-weon Jung
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paper_authors.user: jungjee
paper_authors._id: 66d533861ba71ac4c040eb72
paper_authors.name: Daniel Garcia-Romero
paper_authors.status: admin_assigned
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paper_authors._id: 63af027b5833626d76714bcd
paper_authors.avatarUrl: /avatars/ab8f4266f9efd5c257597738d6ddcc6d.svg
paper_authors.fullname: Daniel Garcia-Romero
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paper_authors.user: dgromero
paper_authors._id: 66d533861ba71ac4c040eb73
paper_authors.name: Andrew Zisserman
paper_title: The VoxCeleb Speaker Recognition Challenge: A Retrospective
paper_summary: 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
paper_ai_summary: A review of the VoxCeleb Speaker Recognition Challenges (VoxSRC) from 2019 to 2023, detailing performance trends, methodological advancements, and future research directions in speaker verification and diarisation.
paper_ai_keywords: speaker recognition diarisation domain adaptation supervised training self-supervised training semi-supervised training evaluation datasets common evaluation dataset