People are poorly equipped to detect AI-powered voice clones

As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and naturalness. We find human participants cannot consistently identify recordings of AI-generated voices. Specifically, participants perceived the identity of an AI-generated voice to be the same as its real counterpart approximately [Formula: see text] of the time, and correctly identified a voice as AI generated only about [Formula: see text] of the time.

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Publication Details

Journal
Scientific Reports
Published
2025-03-31
DOI
https://doi.org/10.1038/s41598-025-94170-3
Citations
27
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
34.45
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article

People are poorly equipped to detect AI-powered voice clones

Emily A. Cooper, Sarah Barrington, Hany Farid
27 citations
Scientific Reports
Generative Adversarial Networks and Image Synthesis
34.45
article

People are poorly equipped to detect AI-powered voice clones

Emily A. Cooper, Sarah Barrington, Hany Farid
article en
27 citations

Abstract

As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and naturalness. We find human participants cannot consistently identify recordings of AI-generated voices. Specifically, participants perceived the identity of an AI-generated voice to be the same as its real counterpart approximately [Formula: see text] of the time, and correctly identified a voice as AI generated only about [Formula: see text] of the time.

Scientific ReportsVol. 15(1)
University of California, Berkeley (US)
Openalex Percentile: Top 0%
Generative Adversarial Networks and Image Synthesis
34.45
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