mlsanityData to 7 Oct 2026

Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals

arXiv 2007.05979 ↗First posted 12 Jul 2020eess.AS cs.LG cs.SDcited by 22

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Abstract (arXiv)

Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs. The reliability of spoofing CMs is typically gauged using the equal error rate (EER) metric. The primitive EER fails to reflect application requirements and the impact of spoofing and CMs upon ASV and its use as a primary metric in traditional ASV research has long been abandoned in favour of risk-based approaches to assessment. This paper presents several new extensions to the tandem detection cost function (t-DCF), a recent risk-based approach to assess the reliability of spoofing CMs deployed in tandem with an ASV system. Extensions include a simplified version of the t-DCF with fewer parameters, an analysis of a special case for a fixed ASV system, simulations which give original insights into its interpretation and new analyses using the ASVspoof 2019 database. It is hoped that adoption of the t-DCF for the CM assessment will help to foster closer collaboration between the anti-spoofing and ASV research communities.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 5; 2023: 4; 2024: 5; 2025: 2; 2026: 2.

22 papers.

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  1. 2021ASVspoof 2021: accelerating progress in spoofed and deepfake speech detectioncited by 157
  2. 2024ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scalecited by 88
  3. 2023Improved DeepFake Detection Using Whisper Featurescited by 28
  4. 2024XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detectioncited by 27
  5. 2021ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plancited by 18
  6. 2022Defense Against Adversarial Attacks on Audio DeepFake Detectioncited by 12
  7. 2021Style Equalization: Unsupervised Learning of Controllable Generative Sequence Modelscited by 11
  8. 2025BiCrossMamba-ST: Speech Deepfake Detection with Bidirectional Mamba Spectro-Temporal Cross-Attentioncited by 9
  9. 2023Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrogramscited by 4
  10. 2022Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learningcited by 3
  11. 2023t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparatorscited by 3
  12. 2024Generalizing Speaker Verification for Spoof Awareness in the Embedding Spacecited by 3
10 more
  1. 2024Meta-Learning Approaches for Improving Detection of Unseen Speech Deepfakescited by 3
  2. 2022Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verificationcited by 2
  3. 2025A Survey of Threats Against Voice Authentication and Anti-Spoofing Systemscited by 2
  4. 2022Optimizing Tandem Speaker Verification and Anti-Spoofing Systemscited by 1
  5. 2022Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecturecited by 1
  6. 2023Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversioncited by 1
  7. 2024Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examplescited by 1
  8. 2026An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasurescited by 1
  9. 2021Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofingcited by 0
  10. 2026RAT: Reference-Augmented Training for ASV Anti-Spoofingcited by 0