# VoxBlink: A Large Scale Speaker Verification Dataset on Camera

arXiv: https://arxiv.org/abs/2308.07056 · HTML: https://mlsanity.com/paper/2308.07056/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2023.

## Built on by (most cited first, 9 of 9)

- [ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models](https://mlsanity.com/paper/2401.17230/) (2024)
- [SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State Space Model](https://mlsanity.com/paper/2411.07751/) (2024)
- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis](https://mlsanity.com/paper/2505.20506/) (2025)
- [CausalVE: Face Video Privacy Encryption via Causal Video Prediction](https://mlsanity.com/paper/2409.19306/) (2024)
- [Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models](https://mlsanity.com/paper/2506.13300/) (2025)
- [Self-Supervised Learning for Speaker Recognition: A study and review](https://mlsanity.com/paper/2602.10829/) (2026)
- [L-Proto: Language-Aware Episodic Prototypical Training for Multilingual Speaker Verification](https://mlsanity.com/paper/2606.17416/) (2026)
- [Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification](https://mlsanity.com/paper/2606.22369/) (2026)

## Abstract (arXiv)

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