The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description
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