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

翻译：本文提出了一个大规模、高质量的视听说话人验证数据集VoxBlink。我们设计了一种创新且鲁棒的自动视听数据挖掘流水线来构建该数据集，该数据集包含来自38K个说话人的145万条语音。由于自动数据收集的固有特性，引入噪声数据不可避免。因此，我们采用多模态净化步骤生成更干净的VoxBlink版本，名为VoxBlink-clean，包含1.8万个身份和102万条语音。与VoxCeleb不同，VoxBlink的数据源来自普通用户的短视频，所覆盖的场景更贴近真实生活。据我们所知，VoxBlink数据集是目前公开的最大的说话人验证数据集之一。通过联合使用VoxCeleb和VoxBlink-clean数据集，我们采用多种架构骨干的说话人验证模型，在VoxCeleb测试集上进行了全面评估。实验结果表明，将VoxBlink-clean纳入训练后，不同骨干架构的性能相对提升了12%至30%。数据集详情请访问http://voxblink.github.io。