Synvox2: Towards A Privacy-Friendly Voxceleb2 Dataset
Xiaoxiao Miao, Xin Wang, Erica Cooper, Junichi Yamagishi, Nicholas W. D. Evans, Massimiliano Todisco, Jean-François Bonastre, Mickael Rouvier
Abstract
The success of deep learning in speaker recognition relies heavily on the use of large datasets. However, the data-hungry nature of deep learning methods has already being questioned on account the ethical, privacy, and legal concerns that arise when using large-scale datasets of natural speech collected from real human speakers. For example, the widely-used VoxCeleb2 dataset for speaker recognition is no longer accessible from the official website. To mitigate these concerns, this work presents an initiative to generate a privacyfriendly synthetic VoxCeleb2 dataset that ensures the quality of the generated speech in terms of privacy, utility, and fairness. We also discuss the challenges of using synthetic data for the downstream task of speaker verification.
BibTeX
@inproceedings{icassp2024_synvox2towardsap,
title = {Synvox2: Towards A Privacy-Friendly Voxceleb2 Dataset},
author = {Xiaoxiao Miao and Xin Wang and Erica Cooper and Junichi Yamagishi and Nicholas W. D. Evans and Massimiliano Todisco and Jean-François Bonastre and Mickael Rouvier},
booktitle = {ICASSP 2024},
year = {2024}
}