A place for (socio)linguistics in audio deepfake detection and discernment: Opportunities for convergence and interdisciplinary collaboration

Abstract

Deepfakes, particularly audio deepfakes, have become pervasive and pose unique, ever-changing threats to society. This paper reviews the current research landscape on audio deepfakes. We assert that limitations of existing approaches to deepfake detection and discernment are areas where (socio)linguists can directly contribute to helping address the societal challenge of audio deepfakes. In particular, incorporating expert knowledge and developing techniques that everyday listeners can use to avoid deception are promising pathways for (socio)linguistics. Further opportunities exist for developing benevolent applications of this technology through generative AI methods as well.