The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection
Open accessAbstract
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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“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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Citations
Cites
Speaker Verification Using Adapted Gaussian Mixture Models
Reynolds, D. A., Quatieri, T. F., & Dunn, R. B. (2000) · Digital Signal Processing · doi:10.1006/dspr.1999.0361 · Cited by 4,282
Robust text-independent speaker identification using Gaussian mixture speaker models
Reynolds, D. A., & Rose, R. C. (1995) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.365379 · Cited by 2,860
An overview of text-independent speaker recognition: From features to supervectors
Kinnunen, T., & Li, H. (2009) · Speech Communication · doi:10.1016/j.specom.2009.08.009 · Cited by 1,451
Self-citationCalculation of a constant Q spectral transform
Brown, J. T. (1991) · The Journal of the Acoustical Society of America · doi:10.1121/1.400476 · Cited by 925
Speaker Recognition by Machines and Humans: A tutorial review
Hansen, J. H. L., & Hasan, T. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2462851 · Cited by 652
Spoofing and countermeasures for speaker verification: A survey
Wu, Z., Evans, N., Kinnunen, T., Yamagishi, J., Alegre, F., & Li, H. (2015) · Speech Communication · doi:10.1016/j.specom.2014.10.005 · Cited by 601
Self-citationASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanilçi, C., Sahidullah, M., & Sizov, A. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-462 · Cited by 484 · cited 1×
“ASVspoof 2017 follows two special sessions on spoofing and countermeasures for automatic speaker verification at INTERSPEECH 2013 [13] and 2015 [14] which formed the first evaluation, ASVspoof 2015 [15].”
BackgroundSelf-citationConstant Q Cepstral Coefficients: A Spoofing Countermeasure for Automatic Speaker Verification
Todisco, M., Delgado, H., & Evans, N. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.01.001 · Cited by 424
Self-citationA New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients
Todisco, M., Delgado, H., & Evans, N. (2016) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2016-41 · Cited by 340
Self-citationText-dependent speaker verification: Classifiers, databases and RSR2015
Larcher, A., Lee, K. A., Ma, B., & Li, H. (2014) · Speech Communication · doi:10.1016/j.specom.2014.03.001 · Cited by 239
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ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
Todisco, M., Wang, X., Vestman, V., Sahidullah, M., Delgado, H., Nautsch, A., Yamagishi, J., Evans, N., Kinnunen, T. H., & Lee, K. A. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2249 · Cited by 687
Self-citationASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101114 · Cited by 505
Self-citationAASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks
Jung, J.-W., Heo, H.-S., Tak, H., Shim, H.-J., Chung, J. S., Lee, B.-J., Yu, H.-J., & Evans, N. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747766 · Cited by 399
Self-citationASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
Yamagishi, J., Wang, X., Todisco, M., Sahidullah, M., Patino, J., Nautsch, A., Liu, X., Lee, K. A., Kinnunen, T., Evans, N., et al. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-8 · Cited by 380
Self-citationAn Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning
Şişman, B., Yamagishi, J., King, S., & Li, H. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3038524 · Cited by 399
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Metadata
- 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
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