  Large-scale pre-trained language models (PLMs) have shown great potential in
natural language processing tasks. Leveraging the capabilities of PLMs to
enhance automatic speech recognition (ASR) systems has also emerged as a
promising research direction. However, previous works may be limited by the
inflexible structures of PLMs and the insufficient utilization of PLMs. To
alleviate these problems, we propose the hierarchical knowledge distillation
(HKD) on the continuous integrate-and-fire (CIF) based ASR models. To transfer
knowledge from PLMs to the ASR models, HKD employs cross-modal knowledge
distillation with contrastive loss at the acoustic level and knowledge
distillation with regression loss at the linguistic level. Compared with the
original CIF-based model, our method achieves 15% and 9% relative error rate
reduction on the AISHELL-1 and LibriSpeech datasets, respectively.
