Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection
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Abstract (arXiv)
Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks. As evidenced by the latest ASVspoof 2019 countermeasure challenge, models currently deployed for the task of ASV are, at their best, devoid of suitable degrees of generalization to unseen attacks. Upon further investigation of the proposed methods, it appears that a broader three-tiered view of the proposed systems. comprised of the classifier, feature extraction phase, and model loss function, may to some extent lessen the problem. Accordingly, the present study proposes the Efficient Attention Branch Network (EABN) modular architecture with a combined loss function to address the generalization problem...
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 5 Sep 2021.
Builds on
17 of 47 references.
References · 17
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5 more
- 2018Attentive Filtering Networks for Audio Replay Attack Detectioncited by 7
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