  In this paper, we introduce a large-scale and high-quality audio-visual
speaker verification dataset, named VoxBlink. We propose an innovative and
robust automatic audio-visual data mining pipeline to curate this dataset,
which contains 1.45M utterances from 38K speakers. Due to the inherent nature
of automated data collection, introducing noisy data is inevitable. Therefore,
we also utilize a multi-modal purification step to generate a cleaner version
of the VoxBlink, named VoxBlink-clean, comprising 18K identities and 1.02M
utterances. In contrast to the VoxCeleb, the VoxBlink sources from short videos
of ordinary users, and the covered scenarios can better align with real-life
situations. To our best knowledge, the VoxBlink dataset is one of the largest
publicly available speaker verification datasets. Leveraging the VoxCeleb and
VoxBlink-clean datasets together, we employ diverse speaker verification models
with multiple architectural backbones to conduct comprehensive evaluations on
the VoxCeleb test sets. Experimental results indicate a substantial enhancement
in performance,ranging from 12% to 30% relatively, across various backbone
architectures upon incorporating the VoxBlink-clean into the training process.
The details of the dataset can be found on http://voxblink.github.io
