  Attention-based encoder-decoder (AED) models have achieved promising
performance in speech recognition. However, because of the end-to-end training,
an AED model is usually trained with speech-text paired data. It is challenging
to incorporate external text-only data into AED models. Another issue of the
AED model is that it does not use the right context of a text token while
predicting the token. To alleviate the above two issues, we propose a unified
method called LST (Learn Spelling from Teachers) to integrate knowledge into an
AED model from the external text-only data and leverage the whole context in a
sentence. The method is divided into two stages. First, in the representation
stage, a language model is trained on the text. It can be seen as that the
knowledge in the text is compressed into the LM. Then, at the transferring
stage, the knowledge is transferred to the AED model via teacher-student
learning. To further use the whole context of the text sentence, we propose an
LM called causal cloze completer (COR), which estimates the probability of a
token, given both the left context and the right context of it. Therefore, with
LST training, the AED model can leverage the whole context in the sentence.
Different from fusion based methods, which use LM during decoding, the proposed
method does not increase any extra complexity at the inference stage. We
conduct experiments on two scales of public Chinese datasets AISHELL-1 and
AISHELL-2. The experimental results demonstrate the effectiveness of leveraging
external text-only data and the whole context in a sentence with our proposed
method, compared with baseline hybrid systems and AED model based systems.
