Papers › Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker...

Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection

5 Sep 2021arXiv:2109.02051archive 2025-07-28

Amir Mohammad Rostami, Mohammad Mehdi Homayounpour, Ahmad Nickabadi

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...

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AmirmohammadRostami/ASV-anti-spoofing-with-EABN officialmentioned on GitHubpytorchMIT report

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Tasks

Speaker VerificationSpoof DetectionVoice Anti-spoofing

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Spoof Detection ASVspoof 2019 - LA Proposed: LFCC+SE-ResABNet+CombLoss EER 1.89 #1 of 1 Archive leaderboard report
Spoof Detection ASVspoof 2019 - LA Proposed: LFCC+SE-ResABNet+CombLoss t-DCF 0.0507 #1 of 1 Archive leaderboard report
Spoof Detection ASVspoof 2019 - PA Proposed: logPowSpec+EABNet+CombLoss EER 0.86 #1 of 1 Archive leaderboard report
Spoof Detection ASVspoof 2019 - PA Proposed: logPowSpec+EABNet+CombLoss t-DCF 0.0239 #1 of 1 Archive leaderboard report

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