Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech.
@article{arxiv.2110.01900,
title = {DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT},
author = {Heng-Jui Chang and Shu-wen Yang and Hung-yi Lee},
journal= {arXiv preprint arXiv:2110.01900},
year = {2022}
}