  In this paper, we propose a simple but effective method to decode the output
of Connectionist Temporal Classifier (CTC) model using a bi-directional neural
language model. The bidirectional language model uses the future as well as the
past information in order to predict the next output in the sequence. The
proposed method based on bi-directional beam search takes advantage of the CTC
greedy decoding output to represent the noisy future information. Experiments
on the Librispeechdataset demonstrate the superiority of our proposed method
compared to baselines using unidirectional decoding. In particular, the boost
inaccuracy is most apparent at the start of a sequence which is the most
erroneous part for existing systems based on unidirectional decoding.
