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People are poorly equipped to detect AI-powered voice clones

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arxiv 2410.03791 v2 pith:EGX5PKQW submitted 2024-10-03 cs.HC cs.AIcs.CYcs.SDeess.AS

Sarah Barrington , Emily A. Cooper , Hany Farid This is my paper
classification cs.HCcs.AIcs.CYcs.SDeess.AS
keywords ai-generatedcontinuesidentityparticipantstimevoicevoicesai-powered
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SecureSpeech: Prompt-based Speaker and Content Protection

    cs.SD 2025-07 conditional novelty 5.0 of 10

    A dual anonymization pipeline that uses an LLM to replace sensitive entities and a prompt-driven TTS to generate speech with a new, unrelated voice.

  2. The Age of Sensorial Zero Trust: Why We Can No Longer Trust Our Senses

    cs.CR 2025-07 unverdicted novelty 3.0 of 10

    A position paper argues that organizations should apply Zero Trust verification principles to human sensory perception to defend against deepfake and voice-clone fraud.

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