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Evaluation of Audio Deepfakes – Systematic Review

  • Generative models for audio are commonly used for music composition, sound effects generation for video game development, audio restoration, voice cloning, etc. The ease of generating indistinguishable fake audio with deep learning poses a major threat to personal privacy, online security, and political discourse. Evaluating the quality and realism of these synthetic utterances is crucial for mitigating the potential for misinformation and harm. To assess this threat, this paper conducts a systematic review, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), on how these deepfake models are currently evaluated. The analysis of 86 papers shows that the majority of the evaluation is conducted on a machine level and highlights a research gap regarding the human perception of deepfakes. This paper explores various methods and perceptual measures employed in assessing audio deepfakes and evaluating their strengths, limitations, and future directions.

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Metadaten
Author:Yamini Sinha, Jan Hintz, Ingo Siegert
URN:urn:nbn:de:bvb:898-opus4-70960
DOI:https://doi.org/10.35096/othr/pub-7096
ISBN:978-3-95908-325-6
Parent Title (German):Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024
Publisher:TUDpress
Place of publication:Dresden
Editor:Timo Baumann
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2024
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2024/03/08
First Page:181
Last Page:187
Andere Schriftenreihe:Studientexte zur Sprachkommunikation ; 107
Institutes:Fakultät Informatik und Mathematik
research focus:Information und Kommunikation
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG