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- W4384652389 abstract "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." @default.
- W4384652389 created "2023-07-19" @default.
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- W4384652389 date "2023-07-14" @default.
- W4384652389 modified "2023-10-14" @default.
- W4384652389 title "Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features" @default.
- W4384652389 doi "https://doi.org/10.48550/arxiv.2307.07683" @default.
- W4384652389 hasPublicationYear "2023" @default.
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