Cited by 60Speech Recognition and SynthesisSpeech and Audio ProcessingBiometric Identification and Security

A study on data augmentation in voice anti-spoofing

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Cohen, Ariel, Rimon, Inbal, Aflalo, Eran, Permuter, Haim H.·2022·Speech Communication

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Crossref · 10.1016/j.specom.2022.04.005

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47 of 47 references verified0 citation errors · 0 not checkable

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  • E2E architectures and SSL-based frontends have led to substantial improvements in performance for the LA task, even for single systems (not ensembles), with best t-DCF being 0.2066 (best EER 0.82%) for the ASVspoof 2021 LA database.

    “While conventional acoustic features remain popular [45], E2E architectures and SSL-based frontends have led to substantial improvements in performance for the LA task, even for single systems (not ensembles), with best t-DCF being 0.2066 (best EER 0.82%) for the ASVspoof 2021 LA database [42].”
  • Data augmentation techniques substantially improve the robustness of spoofed audio detection.

    “All these studies indicated that the robustness of spoofed audio detection is substantially improved when DA techniques are employed.”
  • Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios.

    “Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios.”

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Authors
Cohen, A., Rimon, I., Aflalo, E., & Permuter, H. H.
Affiliations
Ben-Gurion University of the Negev (IL)
Corresponding author
Cohen, Ariel
Venue
Speech Communication
Publisher
Elsevier BV
Published
2022-06
Volume
141
Pages
56-67
ISSN
0167-6393 (ISSN-L)
OpenAlex
W3207579445
Cited by
60
References
47
Citations by year
2026: 8 · 2025: 19 · 2024: 17 · 2023: 12 · 2022: 4
FWCI
4.28
Type
journal article
Language
en
Open access
Locations
  • Speech Communication (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
Keywords
data augmentation, channel variation, audio compression, spoofing attacks, online data augmentation, voice biometrics, ASVspoof 2021

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