🔎 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)

Results on ASVspoof 5