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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“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
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Self-citationASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge
Wu, Z., Yamagishi, J., Kinnunen, T., Hanilci, C., Sahidullah, M., Sizov, A., Evans, N., Todisco, M., & Delgado, H. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2671435 · Cited by 229
Self-citationSpoofing and countermeasures for automatic speaker verification
Evans, N., Kinnunen, T., & Yamagishi, J. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-288 · Cited by 163
Self-citationA study on replay attack and anti-spoofing for text-dependent speaker verification
Wu, Z., Gao, S., Cling, E. S., & Li, H. (2014) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2014.7041636 · Cited by 149
The reddots data collection for speaker recognition
Lee, K. A., Larcher, A., Wang, G., Kenny, P., Brümmer, N., Leeuwen, D. V., Aronowitz, H., Kockmann, M., Vaquero, C., Ma, B., et al. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-95 · Cited by 136
Self-citationOn the vulnerability of speaker verification to realistic voice spoofing
Ergünay, Ş. S. K., Khoury, E., Lazaridis, A., & Marcel, S. (2015) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2015.7358783 · Cited by 118
Kinnunen, T., Sahidullah, M., Falcone, M., Costantini, L., Hautamäki, R. G., Thomsen, D. A. L., Sarkar, A. K., Tan, Z., Delgado, H., Todisco, M., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953187 · Cited by 107
Self-citationWu, Z., Leon, P. L. D., Demiroğlu, C., Khodabakhsh, A., King, S., Ling, Z.-H., Saito, D., Stewart, B., Toda, T., Wester, M., et al. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2526653 · Cited by 103
Self-citationComparative study of several distortion measures for speech recognition
Nocerino, N., Soong, F. K., Rabiner, L. R., & Klatt, D. H. (1985) · Speech Communication · doi:10.1016/0167-6393(85)90057-3 · Cited by 102
Re-assessing the threat of replay spoofing attacks against automatic speaker verification
Alegre, F., Janicki, A., & Evans, N. (2014) · Graduate School and Research Center in Digital Science (EURECOM) · Cited by 81
Self-citationDeep features for automatic spoofing detection
Qian, Y., Chen, N., & Yu, K. (2016) · Speech Communication · doi:10.1016/j.specom.2016.10.007 · Cited by 69
Playback attack detection for text-dependent speaker verification over telephone channels
Gałka, J., Grzywacz, M., & Samborski, R. (2014) · Speech Communication · doi:10.1016/j.specom.2014.12.003 · Cited by 67
Overview of BTAS 2016 speaker anti-spoofing competition
Korshunov, P., Marcel, S., Muckenhirn, H., Gonçalves, A. R., Mello, A. G. S., Violato, R. P. V., Simões, F. O., Neto, M. U., Angeloni, M. D. A., Stuchi, J. A., et al. (2016) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2016.7791200 · Cited by 67
Self-citationAutomatic verbal information verification for user authentication
Li, Q., Juang, B.-H. F., & Lee, C. (2000) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.861378 · Cited by 51
Generalization of spoofing countermeasures: A case study with ASVspoof 2015 and BTAS 2016 corpora
Paul, D., Sahidullah, M., & Saha, G. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952516 · Cited by 38
Self-citationDelgado, H., Todisco, M., Sahidullah, M., Sarkar, A. K., Evans, N., Kinnunen, T., & Tan, Z. (2016) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2016.7846262 · Cited by 37
Self-citationA local fingerprinting approach for audio copy detection
Esmaeili, M. M., & Ward, R. K. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.11.023 · Cited by 36
Kinnunen, T., Sahidullah, M., Kukanov, I., Delgado, H., Todisco, M., Sarkar, A. K., Thomsen, N. B., Hautamäki, V., Evans, N., & Tan, Z. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1125 · Cited by 34
Self-citationCross-Database Evaluation of Audio-Based Spoofing Detection Systems
Korshunov, P., & Marcel, S. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1326 · Cited by 31
A robust audio fingerprinting method for content-based copy detection
Ouali, C., Dumouchel, P., & Gupta, V. (2014) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2014.6849814 · Cited by 29
Text-Dependent Speaker Recognition With Random Digit Strings
Stafylakis, T., Alam, M. J., & Kenny, P. J. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2546458 · Cited by 27
Flagged first, then by citations
Cited by
Sorted by citations
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
Self-citation
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
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
