SynVox2: Towards a privacy-friendly VoxCeleb2 dataset

TL;DR

This paper introduces SynVox2, a synthetic version of the VoxCeleb2 dataset, designed to address privacy and ethical concerns while maintaining utility for speaker recognition tasks.

Contribution

The paper presents a method to generate a privacy-preserving synthetic VoxCeleb2 dataset, enabling ethical and legal use in speaker recognition research.

Findings

Synthetic data maintains comparable performance in speaker verification

Addresses privacy and legal issues associated with real datasets

Highlights challenges in using synthetic data for downstream tasks

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 privacy-friendly 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.