t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
Open accessAbstract
The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certain shortcomings. First, the CM EER is not necessarily a reliable predictor of performance when ASV and CMs are combined. Second, the EER operating point is ill-suited to user authentication applications, e.g. telephone banking, characterised by a high target user prior but a low spoofing attack prior. We aim to migrate from CM- to ASV-centric assessment with the aid of a new tandem detection cost function (t-DCF) metric. It extends the conventional DCF used in ASV research to scenarios involving spoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss costs for both systems, and (ii) prior probabilities of target and spoof trials (with an implied third, nontarget prior). The study is intended to serve as a self-contained, tutorial-like presentation. We analyse with the t-DCF a selection of top-performing CM submissions to the 2015 and 2017 editions of ASVspoof, with a focus on the spoofing attack prior. Whereas there is little to choose between countermeasure systems for lower priors, system rankings derived with the EER and t-DCF show differences for higher priors. We observe some ranking changes. Findings support the adoption of the DCFbased metric into the roadmap for future ASVspoof challenges, and possibly for other biometric anti-spoofing evaluations.
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Monteiro, J., & Alam, J. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003792 · Cited by 14 · see citing works
Feature Genuinization based Residual Squeeze-and-Excitation for Audio Anti-Spoofing in Sound AI
Ray, R., Karthik, S., Mathur, V., Kumar, P., Maragatham, G., Tiwari, S., & Shankarappa, R. T. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580127 · Cited by 15 · 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-citationRobust Spoof Speech Detection Based on Multi-Scale Feature Aggregation and Dynamic Convolution
Wu, H., Zhang, J., Zhang, Z., Zhao, W., Gu, B., & Guo, W. (2024) · ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp48485.2024.10446612 · Cited by 14 · see citing works
Combining evidences from Hilbert envelope and residual phase for detecting replay attacks
Singh, M., & Pati, D. (2019) · International Journal of Speech Technology · doi:10.1007/s10772-019-09604-x · Cited by 14 · see citing works
Hierarchical multi-source cues fusion for mono-to-binaural based Audio Deepfake Detection
Liu, R., Zhang, J., & Li, H. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103097 · Cited by 13 · see citing works
Replay anti-spoofing countermeasure based on data augmentation with post selection
Zhao, Y., Togneri, R. B., & Sreeram, V. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101115 · Cited by 13 · see citing works
Residual Networks for Resisting Noise: Analysis of an Embeddings-based Spoofing Countermeasure
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Sholokhov, A., Kinnunen, T., Vestman, V., & Lee, K. A. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.101024 · Cited by 11 · see citing works
Self-citationOptimizing Tandem Speaker Verification and Anti-Spoofing Systems
Kanervisto, A., Hautamäki, V., Kinnunen, T., & Yamagishi, J. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3138681 · Cited by 12 · see citing works
Self-citationDeepLASD countermeasure for logical access audio spoofing
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Uncovering the Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects
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Discriminative features based on modified log magnitude spectrum for playback speech detection
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A Practical Guide to Logical Access Voice Presentation Attack Detection
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Self-citationFang, F., Yamagishi, J., Echizen, I., Sahidullah, M., & Kinnunen, T. (2018) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2018.8630764 · Cited by 9 · see citing works
Self-citationA Novel Approach Towards Generalization of Countermeasure for Spoofing Attack on ASV Systems
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A multi-branch ResNet with discriminative features for detection of replay speech signals
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Generation and Detection of Media Clones
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Self-citation
- Authors
- Kinnunen, T., Lee, K. A., Delgado, H., Evans, N., Todisco, M., Sahidullah, M., Yamagishi, J., & Reynolds, D. A.
- Affiliations
- University of Eastern Finland (FI)
- NEC (Japan) (JP)
- EURECOM (FR)
- Institut national de recherche en sciences et technologies du numérique (FR)
- National Institute of Informatics (JP)
- Massachusetts Institute of Technology (US)
- ORCID
- Venue
- The Speaker and Language Recognition Workshop (Odyssey 2018)
- Publisher
- ISCA
- Published
- 2018-06-26
- Pages
- 312-319
- OpenAlex
- W2799053639
- Cited by
- 198
- References
- 13
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 20 · 2023: 25 · 2022: 27 · 2021: 33 · 2020: 34 · 2019: 23 · 2018: 2
- FWCI
- 9.35
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- Locations
- The Speaker and Language Recognition Workshop (Odyssey) (publishedVersion)
- HAL (submittedVersion, other-oa)
- Graduate School and Research Center in Digital Science (EURECOM) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- 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
- spoofing countermeasures, speaker verification, spoofing attacks, speaker verification security
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