English

The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description

Sound 2021-09-09 v3 Machine Learning Audio and Speech Processing

Abstract

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.

Keywords

Cite

@article{arxiv.2109.02052,
  title  = {The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description},
  author = {Josef Slavíček and Albert Swart and Michal Klčo and Niko Brümmer},
  journal= {arXiv preprint arXiv:2109.02052},
  year   = {2021}
}

Comments

Second place in the self-supervised track of VoxSRC-21: VoxCeleb Speaker Recognition Challenge