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Efficient and Robust Speaker Diarization via Structured Pruning of Self-Supervised Models
This work presents a framework for compressing self-supervised models for speaker diarization through structured pruning guided by knowledge distillation. We investigate pruning objectives that target reducing both model parameters and computational complexity, where knowledge distillation enables c
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