Impact of Bandwidth and Channel Variation on Presentation Attack Detection for Speaker Verification
Abstract
Vulnerabilities to presentation attacks can undermine confidence in automatic speaker verification (ASV) technology. While efforts to develop countermeasures, known as presentation attack detection (PAD) systems, are now under way, the majority of past work has been performed with high-quality speech data. Many practical ASV applications are narrowband and encompass various coding and other channel effects. PAD performance is largely untested in such scenarios. This paper reports an assessment of the impact of bandwidth and channel variation on PAD performance. Assessments using two current PAD solutions and two standard databases show that they provoke significant degradations in performance. Encouragingly, relative performance improvements of 98% can nonetheless be achieved through feature optimisation. This performance gain is achieved by optimising the spectro-temporal decomposition in the feature extraction process to compensate for narrowband speech. However, compensating for channel variation is considerably more challenging.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Ideal clean conditions are unrealistic and other work has shown the likely degradation in countermeasure (CM) performance when deployed in practical scenarios such as person authentication over telephony channels.
“Such ideal conditions are unrealistic and other work has shown the likely degradation in CM performance when they are deployed in practical scenarios such as person authentication over telephony channels [16], [17].”
From ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2023) · cites this paper as [16]
Cites
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 · see citing works
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 · see citing works
Self-citationSpeaker 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 629 · see citing works
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 · see citing works
Self-citationThe ASVspoof 2017 challenge: assessing the limits of replay spoofing attack detection
Kinnunen, T., Sahidullah, M., Delgado, H., Todisco, M., Evans, N., Yamagishi, J., & Lee, K. A. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1111 · Cited by 535 · see citing works
Self-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 · see citing works
Self-citationA comparison of features for synthetic speech detection
Sahidullah, M., Kinnunen, T., & Hanilçi, C. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-472 · Cited by 382 · see citing works
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 · see citing works
Self-citationPsychological Foundations of Musical Behavior
Jones, M. R., Radocy, R. E., & Boyle, J. D. (1990) · The American Journal of Psychology · doi:10.2307/1423149 · Cited by 274 · see citing works
ASVspoof: 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 · see citing works
Self-citationPatel, T. B., & Patil, H. A. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-467 · Cited by 167 · see citing works
Spoofing and countermeasures for automatic speaker verification
Evans, N., Kinnunen, T., & Yamagishi, J. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-288 · Cited by 163 · see citing works
Self-citationThe 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 · see citing works
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
Delgado, H., Evans, N., Kinnunen, T., Lee, K. A., Liu, X., Nautsch, A., Patino, J., Sahidullah, M., Todisco, M., Wang, X., et al. (2021) · arXiv · doi:10.48550/arxiv.2109.00535 · Cited by 129 · see citing works
Self-citation“Preventing replay attacks on speaker verification systems,”
Villalba, J., & Lleida, E. (2011) · 2011 Carnahan Conference on Security Technology · doi:10.1109/ccst.2011.6095943 · Cited by 108 · see citing works
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 · see citing works
Self-citationAutomatic versus human speaker verification: The case of voice mimicry
Hautamäki, R. G., Kinnunen, T., Hautamäki, V., & Laukkanen, A.-M. (2015) · Speech Communication · doi:10.1016/j.specom.2015.05.002 · Cited by 91 · see citing works
Self-citationConstant-Q signal analysis and synthesis
Youngberg, J., & Boll, S. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1978.1170547 · Cited by 77 · see citing works
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 · see citing works
Hanilçi, C., Kinnunen, T., Sahidullah, M., & Сизов, А. С. (2016) · Speech Communication · doi:10.1016/j.specom.2016.10.002 · Cited by 63 · see citing works
Self-citationEffect of multi-condition training and speech enhancement methods on spoofing detection
Yu, H. T., Sarkar, A. K., Thomsen, D. A. L., Tan, Z., Ma, Z., & Guo, J. H. (2016) · International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) · doi:10.1109/splim.2016.7528399 · Cited by 32 · see citing works
An Investigation of Spoofing Speech Detection Under Additive Noise and Reverberant Conditions
Tian, X., Wu, Z., Xiao, X., Chng, E. S., & Li, H. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-743 · Cited by 23 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild
Liu, X., Wang, X., Sahidullah, M., Patino, J., Delgado, H., Kinnunen, T., Todisco, M., Yamagishi, J., Evans, N., Nautsch, A., et al. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2023.3285283 · Cited by 312 · see citing works
Self-citationIntroduction to Voice Presentation Attack Detection and Recent Advances
Sahidullah, M., Delgado, H., Todisco, M., Kinnunen, T., Evans, N., Yamagishi, J., & Lee, K. A. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-92627-8_15 · Cited by 70 · see citing works
Self-citationKinnunen, T., Lorenzo-Trueba, J., Yamagishi, J., Toda, T., Saito, D., Villavicencio, F., & Ling, Z.-H. (2018) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2018-27 · Cited by 25 · see citing works
Self-citationDeep domain adaptation for anti-spoofing in speaker verification systems
Himawan, I., Villavicencio, F., Sridharan, S., & Fookes, C. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.05.007 · Cited by 17 · see citing works
Introduction to Voice Presentation Attack Detection and Recent Advances
Sahidullah, M., Delgado, H., Todisco, M., Nautsch, A., Wang, X., Kinnunen, T., Evans, N., Yamagishi, J., & Lee, K. A. (2023) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-19-5288-3_13 · Cited by 15 · see citing works
Self-citationPhonespoof: A New Dataset for Spoofing Attack Detection in Telephone Channel
Lavrentyeva, G., Novoselov, S., Volkova, M., Matveev, Y., & De Marsico, M. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682942 · Cited by 13 · see citing works
NMF Based System for Speaker Identification
Costantini, G., Cesarini, V., & Paolizzo, F. (2021) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot51437.2021.9488491 · Cited by 1 · see citing works
Detection of spoofing attacks on speaker verification systems in telephone channel
Lavrentyeva, G. (2018) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2018-18-4-663-668
- Authors
- Delgado, H., Todisco, M., Evans, N., Sahidullah, M., Liu, W. M., Alegre, F., Kinnunen, T., & Fauve, B.
- Affiliations
- EURECOM (FR)
- University of Eastern Finland (FI)
- Venue
- 2017 International Conference of the Biometrics Special Interest Group (BIOSIG)
- Publisher
- IEEE
- Published
- 2017-09
- Volume
- 3
- Pages
- 1-6
- OpenAlex
- W2756766252
- Cited by
- 8
- References
- 22
- Citations by year
- 2023: 2 · 2021: 1 · 2019: 3 · 2018: 2
- FWCI
- 0.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference of the Biometrics Special Interest Group (BIOSIG) (publishedVersion)
- Graduate School and Research Center in Digital Science (EURECOM) (submittedVersion)
- 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, narrowband speech, channel variation, bandwidth effects, feature extraction, speech coding
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
