  Much research effort is being applied to the task of compressing the
knowledge of self-supervised models, which are powerful, yet large and memory
consuming. In this work, we show that the original method of knowledge
distillation (and its more recently proposed extension, decoupled knowledge
distillation) can be applied to the task of distilling HuBERT. In contrast to
methods that focus on distilling internal features, this allows for more
freedom in the network architecture of the compressed model. We thus propose to
distill HuBERT's Transformer layers into an LSTM-based distilled model that
reduces the number of parameters even below DistilHuBERT and at the same time
shows improved performance in automatic speech recognition.

Audio and Speech Processing;Machine Learning;Sound;Signal Processing