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The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description

  • arXiv (Cornell University)
  • Cornell University
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We describe the Phonexia submission for the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21) in the unsupervised speaker verification track. Our solution was very similar to IDLab's winning submission for VoxSRC-20. An embedding extractor was bootstrapped using momentum contrastive learning, with input augmentations as the only source of supervision. This was followed by several iterations of clustering to assign pseudo-speaker labels that were then used for supervised embedding extractor training. Finally, a score fusion was done, by averaging the zt-normalized cosine scores of five different embedding extractors. We briefly also describe unsuccessful solutions involving i-vectors instead of DNN embeddings and PLDA instead of cosine scoring.

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Publication details

DOI
10.48550/arxiv.2109.02052
OpenAlex
W3196906655
Document type
preprint
Language
EN
Source
arXiv (Cornell University)
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