SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
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The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scenario by introducing spoofed trials to the usual set of target and impostor trials. In contrast to the established ASVspoof challenge where the focus is upon separate, independently optimised spoofing detection and speaker verification sub-systems, SASV targets the development of integrated and jointly optimised solutions. Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions. Both models and baselines are freely available to participants and can be used to develop back-end fusion approaches or end-to-end solutions. Using the provided common evaluation protocol, 23 teams submitted SASV solutions. When assessed with target, bona fide non-target and spoofed non-target trials, the top-performing system reduces the equal error rate of a conventional speaker verification system from 23.83% to 0.13%. SASV challenge results are a testament to the reliability of today's state-of-the-art approaches to spoofing detection and speaker verification.
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SASV challenge results have shown the need for larger labeled datasets for learning speaker embedding extractors and end-to-end solutions.
“SASV challenge results [52] have shown the need for larger labeled datasets for the learning of speaker embedding extractors and end-to-end solutions.”
From ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2023) · cites this paper as [52]
The Discussion and Conclusions (Section 7; page not provided) say that future integrated, single-system approaches will likely depend on larger databases with more speakers and more diverse spoofing attacks. The paper does not specifically say that larger labeled datasets are needed to train speaker embedding extractors; Section 3 instead describes the existing database used to train ASV subsystems. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSASV challenge results anticipate the significance of improving the reliability of both ASV and CM sub-systems, including joint optimization schemes.
“A summary of SASV results [52] anticipates the significance of improving the reliability of both ASV and CM sub-systems, including the joint optimization schemes.”
From ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2023) · cites this paper as [52]
Track 2 takes a step forward from EER-based metrics used in the first SASV challenge to DCF-based metrics.
“Track 2 takes a step forward from EER-based metrics used in the first SASV challenge [6] to DCF-based metrics.”
From ASVspoof 5: crowdsourced speech data, deepfakes, and adversarial attacks at scale (2024) · cites this paper as [6]
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End-to-End Modeling for Speech Spoofing and Deepfake Detection
Tak, H. (2023) · theses.fr (ABES) · doi:10.70675/aa9fb6cbz3f6az45bcz933fzf95d89893e59
Self-citationLee, Y., Doan, T.-P., Hong, S., & Jung, S. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3744
Joint Optimization of ASV and CM Tasks: Btuef Team’s Submission for WildSpoof Challenge
Kurnaz, O., Mishra, J., Kinnunen, T., & Hanilçi, C. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461784
Self-citationWu, Y., Jung, J.-W., Shim, H.-J., Cheng, X., & Wang, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461279
Self-citationShort-Utterance Authentication using Lexical Challenge and Speaker Verification
Snow, E., Bhattarakosol, P., & Luangsodsai, A. (2026) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse68839.2026.11597046
Multi-Task Arabic Speech Modeling for Secure Voice Commands
Zitouni, A., Ferhat, R., & Hadjadji, I. (2026) · International Conference on Speech, Multimodal and Advanced Communication Systems (ICSMACS) · doi:10.1109/icsmacs68968.2026.11628592
Active Acoustic Probes for AI Voice Call Detection
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Algorithmic Detection of AI-Generated and Spoofed Speech for Interpretable Voice Authentication
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- Authors
- Jung, J.-W., Tak, H., Shim, H.-J., Heo, H.-S., Lee, B.-J., Chung, S.-W., Yu, H.-J., Evans, N., & Kinnunen, T.
- Affiliations
- Naver (South Korea) (KR)
- EURECOM (FR)
- University of Seoul (KR)
- University of Eastern Finland (FI)
- ORCID
- Venue
- Interspeech 2022
- Publisher
- ISCA
- Published
- 2022-09-18
- Pages
- 2893-2897
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W4297841768
- Cited by
- 56
- References
- 34
- Citations by year
- 2026: 11 · 2025: 16 · 2024: 12 · 2023: 13 · 2022: 4
- FWCI
- 6.22
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- anti-spoofing, speaker verification, ASVspoof
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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