  The recent emergence of joint CTC-Attention model shows significant
improvement in automatic speech recognition (ASR). The improvement largely lies
in the modeling of linguistic information by decoder. The decoder
joint-optimized with an acoustic encoder renders the language model from
ground-truth sequences in an auto-regressive manner during training. However,
the training corpus of the decoder is limited to the speech transcriptions,
which is far less than the corpus needed to train an acceptable language model.
This leads to poor robustness of decoder. To alleviate this problem, we propose
linguistic-enhanced transformer, which introduces refined CTC information to
decoder during training process, so that the decoder can be more robust. Our
experiments on AISHELL-1 speech corpus show that the character error rate (CER)
is relatively reduced by up to 7%. We also find that in joint CTC-Attention ASR
model, decoder is more sensitive to linguistic information than acoustic
information.
