A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis

• URL: http://arxiv.org/abs/2606.08669v1
• Date: Sun, 07 Jun 2026 15:20:38 GMT
• Title: A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis
• Abstract summary: We compare hierarchical local feature extraction of ResNet with the global sequence and relational modeling of attention and graph-based back-ends.<n>We expose a domain bias within the ASVspoof 5 dataset, showing that naive data scaling actively degrades performance.<n>Cross-linguistic analysis reveals that fine-tuning with just 8 hours of target-language data enhances detection robustness.
• Score: 7.166778372225167
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To investigate these unpredictable fluctuations, we conduct a comprehensive benchmark of four self-supervised learning feature extractors paired with four back-end classifiers. We compare the hierarchical local feature extraction of ResNet with the global sequence and relational modeling of attention and graph-based back-ends. Through multi-corpus training across three scenarios and six evaluation datasets, our empirical analysis yields two critical findings. First, we expose a domain bias within the ASVspoof 5 dataset, showing that naive data scaling actively degrades performance. Second, our cross-linguistic analysis reveals that fine-tuning with just 8 hours of target-language data enhances detection robustness. Together, these findings emphasize the critical need for domain-aware and language-specific adaptation in spoofing detection.

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