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        <title>Improving Black-box Speech Recognition using Semantic Parsing</title>
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        <namePart type="given">Rodolfo</namePart>
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        <namePart type="given">Jesse</namePart>
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    <name type="personal">
        <namePart type="given">Raymond</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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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)

• Improving Black-box Speech Recognition using Semantic Parsing (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.