Abstract
Speech is a natural channel for human-computer interaction in robotics and consumer applications. Natural language understanding pipelines that start with speech can have trouble recovering from speech recognition errors. Black-box automatic speech recognition (ASR) systems, built for general purpose use, are unable to take advantage of in-domain language models that could otherwise ameliorate these errors. In this work, we present a method for re-ranking black-box ASR hypotheses using an in-domain language model and semantic parser trained for a particular task. Our re-ranking method significantly improves both transcription accuracy and semantic understanding over a state-of-the-art ASR’s vanilla output.- Anthology ID:
- I17-2021
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 122–127
- Language:
- URL:
- https://aclanthology.org/I17-2021
- DOI:
- Cite (ACL):
- Rodolfo Corona, Jesse Thomason, and Raymond Mooney. 2017. Improving Black-box Speech Recognition using Semantic Parsing. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 122–127, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Improving Black-box Speech Recognition using Semantic Parsing (Corona et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/I17-2021.pdf