ASVspoof2019 vs. ASVspoof5: Assessment and Comparison
원문 초록
ASVspoof challenges are designed to advance the understanding of spoofing speech attacks and encourage the development of robust countermeasure systems. These challenges provide a standardized database for assessing and comparing spoofing-robust automatic speaker verification solutions. The ASVspoof5 challenge introduces a shift in database conditions compared to ASVspoof2019. While ASVspoof2019 has mismatched conditions only in spoofing attacks in the evaluation set, ASVspoof5 incorporates mismatches in both bona fide and spoofed speech statistics. This paper examines the impact of these mismatches, presenting qualitative and quantitative comparisons within and between the two databases. We show the increased difficulty for genuine and spoofed speech and demonstrate that in ASVspoof5, not only are the attacks more challenging, but the genuine speech also shifts toward spoofed speech compared to ASVspoof2019.
함께 살펴볼 논문
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SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing
Tandem spoofing-robust automatic speaker verification based on time-domain embeddings
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
Pushing the Frontiers of Self-Distillation Prototypes Network with Dimension Regularization and Score Normalization
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
토론
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