CAAD internalizes contrastive audio-aware decoding into student SLM weights via synchronized teacher-forcing, delivering an 8% relative gain over standard knowledge distillation on Dynamic-SUPERB while reducing linguistic bias on MCR-BENCH.
We propose an optimal hyperparameter config- uration for this approach; and (3)Standard KD (Std
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CAAD: Contrastive Audio-Aware Distillation for Efficient Speech Language Models
CAAD internalizes contrastive audio-aware decoding into student SLM weights via synchronized teacher-forcing, delivering an 8% relative gain over standard knowledge distillation on Dynamic-SUPERB while reducing linguistic bias on MCR-BENCH.