Pitch Imperfect: Detecting Audio Deepfakes Through Acoustic Prosodic Analysis

Abstract

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 \(L_\{\infty\}\) norm attack against the detectors and using attention mechanisms in o

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