  Large-scale speech self-supervised learning (SSL) has emerged to the main
field of speech processing, however, the problem of computational cost arising
from its vast size makes a high entry barrier to academia. In addition,
existing distillation techniques of speech SSL models compress the model by
reducing layers, which induces performance degradation in linguistic pattern
recognition tasks such as phoneme recognition (PR). In this paper, we propose
FitHuBERT, which makes thinner in dimension throughout almost all model
components and deeper in layer compared to prior speech SSL distillation works.
Moreover, we employ a time-reduction layer to speed up inference time and
propose a method of hint-based distillation for less performance degradation.
Our method reduces the model to 23.8% in size and 35.9% in inference time
compared to HuBERT. Also, we achieve 12.1% word error rate and 13.3% phoneme
error rate on the SUPERB benchmark which is superior than prior work.
