JYP2024/Wedefense_ASV2025_WavLM_Base_Pruning

🔎 Hybrid Pruning for Anti-Spoofing Results

• Input Feature: Raw waveform (via SSL model)
• Frame Configuration: 150 frames per segment, 20 ms frame shift
• Training Strategy: Jointly optimizing for task performance and model sparsity in a single stage. A warm-up schedule is used where the sparsity target linearly increases from 0 to the final value over the first 5 epochs.
• Evaluation Metrics: minDCF, EER (%)
• Evaluation Sets: Dev / Eval
• Back-end: Multi-Head Factorized Attentive Pooling (MHFA)

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Results on ASVspoof 5

The following table compares the performance of our proposed Hybrid Pruning (HP) single system against other top-performing systems from the official ASVspoof 5 Challenge leaderboard.

System	Dev minDCF	Dev EER (%)	Eval minDCF	Eval EER (%)
Rank 3 (ID:T27, Fusion)	-	-	0.0937	3.42
HP (ours, Single system)	0.0395	1.547	0.1028	3.758
Rank 4 (ID:T23, Fusion)	-	-	0.1124	4.16
Rank 9 (ID:T23, Best single system)	-	-	0.1499	5.56