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

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https://onlinelibrary.wiley.com/doi/abs/10.1111/lnc3.12527

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https://doi.org/10.1111/lnc3.12527
 http://hdl.handle.net/11603/35749

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https://orcid.org/0000-0003-0130-6135

Date

2024-07-09

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journal articles
postprints
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Citation of Original Publication

Mallinson, Christine, Vandana P. Janeja, Chloe Evered, Zahra Khanjani, Lavon Davis, Noshaba Nasir Bhalli, and Kifekachukwu Nwosu. “A Place for (Socio)Linguistics in Audio Deepfake Detection and Discernment: Opportunities for Convergence and Interdisciplinary Collaboration.” Language and Linguistics Compass 18, no. 5 (2024): e12527. https://doi.org/10.1111/lnc3.12527.

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This is the peer reviewed version of the following article: Mallinson, Christine, Vandana P. Janeja, Chloe Evered, Zahra Khanjani, Lavon Davis, Noshaba Nasir Bhalli, and Kifekachukwu Nwosu. “A Place for (Socio)Linguistics in Audio Deepfake Detection and Discernment: Opportunities for Convergence and Interdisciplinary Collaboration.” Language and Linguistics Compass 18, no. 5 (2024): e12527. https://doi.org/10.1111/lnc3.12527., which has been published in final form at https://doi.org/10.1111/lnc3.12527. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

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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.