The ASVspoof 2017 challenge: assessing the limits of replay spoofing attack detection
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The ASVspoof initiative was created to promote the development of countermeasures which aim to protect automatic speaker verification (ASV) from spoofing attacks. The first community-led, common evaluation held in 2015 focused on countermeasures for speech synthesis and voice conversion spoofing attacks. Arguably, however, it is replay attacks which pose the greatest threat. Such attacks involve the replay of recordings collected from enrolled speakers in order to provoke false alarms and can be mounted with greater ease using everyday consumer devices. ASVspoof 2017, the second in the series, hence focused on the development of replay attack countermeasures. This paper describes the database, protocols and initial findings. The evaluation entailed highly heterogeneous acoustic recording and replay conditions which increased the equal error rate (EER) of a baseline ASV system from 1.76% to 30.71%. Submissions were received from 49 research teams, 20 of which improved upon a baseline replay spoofing detector EER of 24.65%, in terms of replay/non-replay discrimination. While largely successful, the evaluation indicates that the quest for countermeasures which are resilient in the face of variable replay attacks remains very much alive.
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Before the ASVspoof Challenge 2017, there were only a couple of research papers on replay attack; after the challenge, more approaches for this attack were researched.
“Additionally, before the ASVspoof Challenge 2017 (Kinnunen et al., 2017; Lavrentyeva et al., 2017), there were only a couple of research papers done on replay attack, and after this challenge, more approaches for this attack were researched (Tom et al., 2018; Pradhan et al., 2019).”
From Audio deepfakes: A survey (2023) · cites this paper as Kinnunen et al., 2017
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EarArray: Defending against DolphinAttack via Acoustic Attenuation
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- Authors
- Kinnunen, T., Sahidullah, M., Delgado, H., Todisco, M., Evans, N., Yamagishi, J., & Lee, K. A.
- Affiliations
- University of Eastern Finland (FI)
- EURECOM (FR)
- National Institute of Informatics (JP)
- Institute for Infocomm Research (SG)
- ORCID
- Venue
- Interspeech 2017
- Publisher
- ISCA
- Published
- 2017-08-20
- Pages
- 2-6
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2745896134
- Cited by
- 535
- References
- 30
- Citations by year
- 2026: 31 · 2025: 50 · 2024: 67 · 2023: 61 · 2022: 76 · 2021: 54 · 2020: 71 · 2019: 72 · 2018: 40 · 2017: 13
- FWCI
- 24.45
- Type
- conference paper
- Language
- en
- Open access
- green
- erepo.uef.fi
- Licence
- other-oa
- Locations
- Interspeech (USB) (publishedVersion)
- UEF eRepo (University of Eastern Finland) (submittedVersion, other-oa)
- Graduate School and Research Center in Digital Science (EURECOM) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Funders
- European Commission (647850, HORIZON2020)
- Ministry of Education, Culture, Sports, Science and Technology (16H06302, 15H01686, 26280066)
- Japan Society for the Promotion of Science (15H01686, 16H06302, 26280066)
- 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
- speaker verification, spoofing attacks, equal error rate
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.
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