Toward Transdisciplinary Approaches to Audio Deepfake Discernment
This addresses the growing problem of audio deepfakes for society, though it presents a conceptual perspective rather than concrete results.
The paper argues that current AI models for audio deepfake detection are inadequate due to their lack of understanding of linguistic variability and human speech complexity, and proposes that incorporating linguistic knowledge through transdisciplinary approaches could lead to more robust detection methods.
This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Intelligence methods and linguistics. With an avalanche of tools for the generation of realistic-sounding fake speech on one side, the detection of deepfakes is lagging on the other. Particularly hindering audio deepfake detection is the fact that current AI models lack a full understanding of the inherent variability of language and the complexities and uniqueness of human speech. We see the promising potential in recent transdisciplinary work that incorporates linguistic knowledge into AI approaches to provide pathways for expert-in-the-loop and to move beyond expert agnostic AI-based methods for more robust and comprehensive deepfake detection.