ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Language and Computation
Detection of cloned voices in realistic Forensic Voice Comparison scenarios
Provisionally accepted
- 1Fundación General (CSIC), Madrid, Spain
- 2Blackvox, Buenos Aires, Argentina
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Abstract Deepfakes and synthetic audio significantly degrade the performance of automatic speaker identification systems commonly used in forensic laboratories. We investigate the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) for detecting cloned voices, ultimately 5 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 10 technologies, underscoring the urgent need for more robust and generalized anti-spoofing systems for forensic applications.
Keywords: MFCCs, Deepfake, forensic speech evidence, Automatic speaker recognition (ASR), Acoustic Data
Received: 01 Aug 2025; Accepted: 24 Oct 2025.
Copyright: © 2025 San Segundo Fernández and Univaso. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Eugenia San Segundo Fernández, eugenia.sansegundo@csic.es
Pedro Univaso, punivaso@blackvox.com.ar
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