2025/02/20 by Kevin M. Warren, Daniel Olszewski, Warren, Kevin +9 · 1 citation
Computer Science · #Audio and Speech Processing (eess.AS) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2502.14726
openalex publication_date 2025/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Audio deepfakes are increasingly in-differentiable from organic speech, often fooling both authentication systems and human listeners. While many techniques use low-level audio features or optimization black-box model training, focusing on the features that humans use to recognize speech will likely be a more long-term robust approach to detection. We explore the use of prosody, or the high-level linguistic features of human speech (e.g., pitch, intonation, jitter) as a more foundational means of detecting audio deepfakes. We develop a detector based on six classical prosodic features and demonstrate that our model performs as well as other baseline models used by the community to detect audio deepfakes with an accuracy of 93% and an EER of 24.7%. More importantly, we demonstrate the benefits of using a linguistic features-based approach over existing models by applying an adaptive adversary using an L∞ norm attack against the detectors and using attention mechanisms in our training for explainability. We show that we can explain the prosodic features that have highest impact on the model's decision (Jitter, Shimmer and Mean Fundamental Frequency) and that other models are extremely susceptible to simple L∞ norm attacks (99.3% relative degradation in accuracy). While overall performance may be similar, we illustrate the robustness and explainability benefits to a prosody feature approach to audio deepfake detection.