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.
Authors
- Emily A. Cooper (ORCID: https://orcid.org/0000-0003-4889-7446)
- Sarah Barrington
- Hany Farid (ORCID: https://orcid.org/0000-0002-6095-8339)
Institutions
- University of California, Berkeley (US)
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