People are poorly equipped to detect AI-powered voice clones
October 03, 2024 Β· Declared Dead Β· π Scientific Reports
"No code URL or promise found in abstract"
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Authors
Sarah Barrington, Emily A. Cooper, Hany Farid
arXiv ID
2410.03791
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CY,
cs.SD,
eess.AS
Citations
30
Venue
Scientific Reports
Last Checked
4 months ago
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-voice to be the same as its real counterpart approximately 80% of the time, and correctly identified a voice as AI generated only about 60% of the time.
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