Experiments indicate that diversity of attack generation methods improves cross-dataset generalization in speech anti-spoofing more than increasing training data scale.
A data-centric ap- proach to generalizable speech deepfake detection
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EnvTriCascade is a tri-stage cascaded framework using mix-consistency detection followed by dual SSL-based five-class classifiers with cross-branch attention and RawBoost augmentation, achieving 0.8266 Macro-F1 on the ESDD2 2026 challenge test set.
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Exploring the Scale and Diversity of Speech Anti-spoofing Datasets: Experiments and Analysis
Experiments indicate that diversity of attack generation methods improves cross-dataset generalization in speech anti-spoofing more than increasing training data scale.
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EnvTriCascade: An Environment-Aware Tri-Stage Cascaded Framework for ESDD2 2026 Challenge
EnvTriCascade is a tri-stage cascaded framework using mix-consistency detection followed by dual SSL-based five-class classifiers with cross-branch attention and RawBoost augmentation, achieving 0.8266 Macro-F1 on the ESDD2 2026 challenge test set.