Improving Black-box Speech Recognition using Semantic Parsing

Rodolfo Corona, Jesse Thomason, Raymond Mooney


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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/I17-2021.pdf