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Connectionist Temporal Classification (CTC) model is a very efficient methodfor modeling sequences, especially for speech data. In order to use CTC modelas an Automatic Speech Recognition (ASR) task, the beam search decoding with anexternal language model like n-gram LM is necessary to obtain reasonableresults. In this paper we analyze the blank label in CTC beam search deeply andpropose a very simple method to reduce the amount of calculation resulting infaster 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 lossof accuracy in LibriSpeech datasets. We prove this method is effective not onlypractically by experiments but also theoretically by mathematical reasoning. Wealso observe that this reduction is more obvious if the accuracy of the modelis higher.

Blank Collapse: Compressing CTC emission for the faster decoding