Synthetic-voice cloning technologies have seen significant advances in recent
years, giving rise to a range of potential harms. From small- and large-scale
financial fraud to disinformation campaigns, the need for reliable methods to
differentiate real and synthesized voices is imperative. We describe three
techniques for differentiating a real from a cloned voice designed to
impersonate a specific person. These three approaches differ in their feature
extraction stage with low-dimensional perceptual features offering high
interpretability but lower accuracy, to generic spectral features, and
end-to-end learned features offering less interpretability but higher accuracy.
We show the efficacy of these approaches when trained on a single speaker's
voice and when trained on multiple voices. The learned features consistently
yield an equal error rate between $0\%$ and $4\%$, and are reasonably robust to
adversarial laundering.

本研究描述了三种方法来区分真实和合成语音，并比较它们的特点和准确性，具有对抗性清洗的鲁棒性，在单个讲话者的声音和多个声音上进行了训练。