Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension

Published in Interspeech, 2018

Chia-Hsuan Lee, Szu-Lin Wu, Chi-Liang Liu, Hung-yi Lee [PDF] [Corpus]


@article{lee2018spoken,
  title={Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension},
  author={Lee, Chia-Hsuan and Wu, Szu-Lin and Liu, Chi-Liang and Lee, Hung-yi},
  journal={Proc. Interspeech 2018},
  pages={3459--3463},
  year={2018}
}

Abstract

Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human. On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans. It’s therefore highly attractive to develop machines which can automatically understand spoken content. In this paper, we propose a new listening comprehension task - Spoken SQuAD. On the new task, we found that speech recognition errors have catastrophic impact on machine comprehension, and several approaches are proposed to mitigate the impact.