  Streaming automatic speech recognition (ASR) models are restricted from
accessing future context, which results in worse performance compared to the
non-streaming models. To improve the performance of streaming ASR, knowledge
distillation (KD) from the non-streaming to streaming model has been studied,
mainly focusing on aligning the output token probabilities. In this paper, we
propose a layer-to-layer KD from the teacher encoder to the student encoder. To
ensure that features are extracted using the same context, we insert auxiliary
non-streaming branches to the student and perform KD from the non-streaming
teacher layer to the non-streaming auxiliary layer. We design a special KD loss
that leverages the autoregressive predictive coding (APC) mechanism to
encourage the streaming model to predict unseen future contexts. Experimental
results show that the proposed method can significantly reduce the word error
rate compared to previous token probability distillation methods.
