One-class Learning Towards Synthetic Voice Spoofing Detection

TL;DR

This paper introduces a one-class learning approach for detecting unknown synthetic voice spoofing attacks, significantly improving detection accuracy without data augmentation.

Contribution

The work presents a novel anti-spoofing system using one-class learning with angular margin, effectively detecting unseen attacks in voice verification.

Findings

Achieved 2.19% EER on ASVspoof 2019 dataset

Outperformed all existing single systems without ensemble methods

Effective in detecting unknown synthetic voice spoofing attacks

Abstract

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. Especially, the fast development of synthetic voice spoofing algorithms is generating increasingly powerful attacks, putting the ASV systems at risk of unseen attacks. In this work, we propose an anti-spoofing system to detect unknown synthetic voice spoofing attacks (i.e., text-to-speech or voice conversion) using one-class learning. The key idea is to compact the bona fide speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Without resorting to any data augmentation methods, our proposed system achieves an equal error rate (EER) of 2.19% on the evaluation set of ASVspoof 2019 Challenge logical access scenario, outperforming all existing single systems (i.e., those without model ensemble).