Improving the readability of class lecture ASR results using a confusion network

This paper presents a method for improving the readability of Automatic Speech Recognition (ASR) results for classroom lectures. Most of the previous research on improving the readability of recognition results focused mainly on manually transcribed texts, and not ASR results. Due to the presence of a large number of domain-dependent words and the casual presentation style, even state-of-the-art recognizers yield a 30-50% word error rate for speech in classroom lectures. Thus, a method for improving the readability of ASR results needs to be robust against recognition errors. In this paper, we propose a novel method for improving the readability based on a machine translation model that uses a confusion network representing multiple hypotheses of the ASR results to achieve robustness against recognition errors. Experimental results show that the proposed method outperforms the baselines in both automatic and manual evaluations.