  Recent years have witnessed great strides in self-supervised learning (SSL)
on the speech processing. The SSL model is normally pre-trained on a great
variety of unlabelled data and a large model size is preferred to increase the
modeling capacity. However, this might limit its potential applications due to
the expensive computation and memory costs introduced by the oversize model.
Miniaturization for SSL models has become an important research direction of
practical value. To this end, we explore the effective distillation of
HuBERT-based SSL models for automatic speech recognition (ASR). First, in order
to establish a strong baseline, a comprehensive study on different student
model structures is conducted. On top of this, as a supplement to the
regression loss widely adopted in previous works, a discriminative loss is
introduced for HuBERT to enhance the distillation performance, especially in
low-resource scenarios. In addition, we design a simple and effective algorithm
to distill the front-end input from waveform to Fbank feature, resulting in 17%
parameter reduction and doubling inference speed, at marginal performance
degradation.
