Forensic deepfake audio detection using segmental speech features

• URL: http://arxiv.org/abs/2505.13847v2
• Date: Mon, 02 Jun 2025 02:02:04 GMT
• Title: Forensic deepfake audio detection using segmental speech features
• Authors: Tianle Yang, Chengzhe Sun, Siwei Lyu, Phil Rose,
• Abstract summary: This study explores the potential of using acoustic features of segmental speech sounds to detect deepfake audio.<n>Certain segmental features commonly used in forensic voice comparison (FVC) are effective in identifying deep-fakes, whereas some global features provide little value.
• Score: 27.29588853432526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores the potential of using acoustic features of segmental speech sounds to detect deepfake audio. These features are highly interpretable because of their close relationship with human articulatory processes and are expected to be more difficult for deepfake models to replicate. The results demonstrate that certain segmental features commonly used in forensic voice comparison (FVC) are effective in identifying deep-fakes, whereas some global features provide little value. These findings underscore the need to approach audio deepfake detection using methods that are distinct from those employed in traditional FVC, and offer a new perspective on leveraging segmental features for this purpose.

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