Transformer-based ASR using multiple-utterance beam-search

Transfomers have improved the state-of-the-art performance in many fields as well as speech recognition. But it is not easy to be used for long sequences. In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested including parallelizing the recognition using batched beam search, detecting end-of-speech based on CTC, restricting CTC prefix score and splitting long speeches into short segments. Experimental results with an 8-hour real-world Korean speech test corpus show that the proposed system can convert speeches into text in less than 3 minutes with 10.73% error rate.