  Non-autoregressive automatic speech recognition (ASR) modeling has received
increasing attention recently because of its fast decoding speed and superior
performance. Among representatives, methods based on the connectionist temporal
classification (CTC) are still a dominating stream. However, the theoretically
inherent flaw, the assumption of independence between tokens, creates a
performance barrier for the school of works. To mitigate the challenge, we
propose a context-aware knowledge transferring strategy, consisting of a
knowledge transferring module and a context-aware training strategy, for
CTC-based ASR. The former is designed to distill linguistic information from a
pre-trained language model, and the latter is framed to modulate the
limitations caused by the conditional independence assumption. As a result, a
knowledge-injected context-aware CTC-based ASR built upon the wav2vec2.0 is
presented in this paper. A series of experiments on the AISHELL-1 and AISHELL-2
datasets demonstrate the effectiveness of the proposed method.
