Distilhubert: Speech Representation Learning by Layer-Wise Distillation of Hidden-Unit Bert
Heng-Jui Chang, Shu-Wen Yang, Hung-yi Lee
Abstract
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.
BibTeX
@inproceedings{icassp2022_distilhubertspee,
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},
booktitle = {ICASSP 2022},
year = {2022}
}