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  3. Forensic Deepfake Audio Detection Using Segmental Speech Features.
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  3. Forensic Deepfake Audio Detection Using Segmental Speech Features.

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Forensic deepfake audio detection using segmental speech features.

Tianle Yang1, Chengzhe Sun2, Siwei Lyu2

  • 1University at Buffalo, Department of Linguistics, Buffalo, 14260, NY, United States.

Forensic Science International
|December 12, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study shows that specific speech sound features can effectively detect audio deepfakes, unlike general audio characteristics. A new speaker-specific method is proposed for more accurate forensic deepfake detection.

Keywords:
Deepfake audio detectionDeepfake speechForensic voice comparisonLikelihood ratio

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Area of Science:

  • Acoustic Phonetics
  • Digital Forensics
  • Artificial Intelligence

Background:

  • Deepfake audio poses a significant challenge to authenticity verification.
  • Current deepfake detection methods often rely on global audio features.
  • Replicating fine-grained articulatory speech characteristics is difficult for deepfake generation models.

Purpose of the Study:

  • To investigate the efficacy of segmental speech sound features for audio deepfake detection.
  • To compare the performance of segmental versus global features in identifying deepfakes.
  • To propose and evaluate a novel speaker-specific framework for deepfake detection.

Main Methods:

  • Analysis of acoustic features of segmental speech sounds.
  • Utilizing features common in forensic voice comparison (FVC).
  • Development and testing of a speaker-specific deepfake detection framework.

    • Main Results:

    • Certain segmental features, particularly those used in FVC, are effective in detecting audio deepfakes.
    • Global audio features showed limited value in distinguishing deepfakes.
    • The proposed speaker-specific framework demonstrated potential advantages over speaker-independent systems.

    Conclusions:

    • Segmental acoustic features offer a promising avenue for audio deepfake detection, distinct from traditional FVC approaches.
    • A speaker-specific detection framework is advantageous for forensic applications requiring high interpretability and sensitivity.
    • Future research should focus on refining speaker-specific models for robust deepfake identification.