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
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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 |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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