ArticleScientific reports2025
People are poorly equipped to detect AI-powered voice clones.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Multilayered Epistemic Disruption in AI-Driven Health Misinformation: Conceptual Framework and Viewpoint.JMIR infodemiology · 2026Article
- Artificial Voice Cloning in School Ecosystems: Ethical Risk, Digital Safety and Emerging Implications for Child and Adolescent Health.International journal of school health · 2026Article
- Human detection of AI-generated faces and voices is not domain-general.Scientific reports · 2026Article
- Munich Sentence (MuSe) Database: Completion norms and audio recordings for 619 German sentences.Behavior research methods · 2026Article
- Virtual vs. standardized patients - a preliminary comparison of advantages and disadvantages of a VR and a conventional learning unit for medical students in child and adolescent psychiatry.BMC medical education · 2026Article
- Mitigating the harms of manipulated media: Confronting deepfakes and digital deception.PNAS nexus · 2025Article
- What you see is not what you get anymore: a mixed-methods approach on human perception of AI-generated images.Frontiers in artificial intelligence · 2025Article
- Voice clones sound realistic but not (yet) hyperrealistic.PloS one · 2025Article
- Masked-speech Recognition Using Human and Synthetic Cloned Speech.Trends in hearingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.