SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning
<|reference_start|>SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning: Self-supervised learning (SSL) has achieved remarkable success across various speech-processing tasks. To enhance its efficiency, previous works often leverage the use of compression techniques. A notable recent attempt is DPHuBERT, which applies joint knowledge distillation (KD) and structured pruning to learn a significantly smaller SSL model. In this paper, we contribute to this research domain by introducing SKILL, a novel method that conducts distillation across groups of layers instead of distilling individual arbitrarily selected layers within the teacher network. The identification of the layers to distill is achieved through a hierarchical clustering procedure applied to layer similarity measures. Extensive experiments demonstrate that our distilled version of WavLM Base+ not only outperforms DPHuBERT but also achieves state-of-the-art results in the 30M parameters model class across several SUPERB tasks.<|reference_end|>
@article{zampierin2024skill:,
    title={SKILL: Similarity-aware Knowledge distILLation for Speech
  Self-Supervised Learning},
    author={Luca Zampierin, Ghouthi Boukli Hacene, Bac Nguyen, Mirco Ravanelli},
    journal={arXiv preprint arXiv:2402.16830},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16830},
    primaryClass={eess.AS cs.CL cs.LG cs.SD}
}