Connectionist Temporal Classification (CTC) model is a very efficient method
for modeling sequences, especially for speech data. In order to use CTC model
as an Automatic Speech Recognition (ASR) task, the beam search decoding with an
external language model like n-gram LM is necessary to obtain reasonable
results. In this paper we analyze the blank label in CTC beam search deeply and
propose a very simple method to reduce the amount of calculation resulting in
faster beam search decoding speed. With this method, we can get up to 78%
faster decoding speed than ordinary beam search decoding with a very small loss
of accuracy in LibriSpeech datasets. We prove this method is effective not only
practically by experiments but also theoretically by mathematical reasoning. We
also observe that this reduction is more obvious if the accuracy of the model
is higher.

本文分析了 CTC 束搜索中的空白标签并提出了一种简单的方法来减少计算量，提高解码速度，实验和理论都证明了该方法的有效性，并且发现该方法在模型准确率更高时效果更加明显，适用于 ASR 领域。