@inproceedings{corona-etal-2017-improving,
title = "Improving Black-box Speech Recognition using Semantic Parsing",
author = "Corona, Rodolfo and
Thomason, Jesse and
Mooney, Raymond",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2021",
pages = "122--127",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Black-box Speech Recognition using Semantic Parsing
%A Corona, Rodolfo
%A Thomason, Jesse
%A Mooney, Raymond
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F corona-etal-2017-improving
%X 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.
%U https://aclanthology.org/I17-2021
%P 122-127
Markdown (Informal)
[Improving Black-box Speech Recognition using Semantic Parsing](https://aclanthology.org/I17-2021) (Corona et al., IJCNLP 2017)
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.