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.

本文提出了一种名为 FitHuBERT 的新型语音自监督学习方法，通过在几乎所有模型组件上缩小尺寸并增加详细层级，并通过提示辅助蒸馏方法减少性能退化，将模型压缩至 HuBERT 大小的 23.8％和推理时间的 35.9％，在超级基准测试中实现 12.1％的字错误率和 13.3％的音素误差率。