  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).
