{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T11:59:32Z","timestamp":1763035172626,"version":"3.45.0"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Deepfakes and synthetic audio significantly degrade the performance of automatic speaker recognition systems commonly used in forensic laboratories. We investigate the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) for detecting cloned voices, ultimately concluding that MFCC-based methods are insufficient as a universal anti-spoofing tool due to their inability to generalize across different cloning algorithms. Furthermore, we evaluate the performance of the HIVE AI-deepfake Content Detection tool, noting its vulnerability to babble noise and signal saturation, which are common in real-world forensic recordings. This investigation emphasizes the ongoing competition between voice cloning and detection technologies, underscoring the urgent need for more robust and generalized anti-spoofing systems for forensic applications.<\/jats:p>","DOI":"10.3389\/frai.2025.1678043","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T11:56:16Z","timestamp":1763034976000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of cloned voices in realistic forensic voice comparison scenarios"],"prefix":"10.3389","volume":"8","author":[{"given":"Pedro","family":"Univaso","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugenia","family":"San Segundo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2308.12734","article-title":"Real-time detection of ai-generated speech for deepfake voice conversion","author":"Bird","year":"2023","journal-title":"arXiv"},{"key":"ref2","volume-title":"Praat. 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Tools Appl."},{"key":"ref17","author":"Univaso","year":"2020"},{"key":"ref18","article-title":"Audio deepfake detection: A survey","author":"Yi","year":"2023","journal-title":"arXiv"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2104.01320","article-title":"An empirical study on channel effects for synthetic voice spoofing countermeasure systems","author":"Zhang","year":"2021","journal-title":"arXiv"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1678043\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T11:56:18Z","timestamp":1763034978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1678043\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":19,"alternative-id":["10.3389\/frai.2025.1678043"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1678043","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,13]]},"article-number":"1678043"}}