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Found 3 papers, 3 papers with code
STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models
Albeit great performance of Transformer-based speech selfsupervised learning (SSL) models, their large parameter size and computational cost make them unfavorable to utilize.
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation
Transformer-based speech self-supervised learning (SSL) models, such as HuBERT, show surprising performance in various speech processing tasks.
FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech Self-Supervised Learning
Our method reduces the model to 23. 8% in size and 35. 9% in inference time compared to HuBERT.
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