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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In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), pp. 122-127, Taipei, Taiwan, November 2017.

@inproceedings{corona:ijcnlp17, title={Improving Black-box Speech Recognition using Semantic Parsing}, author={Rodolfo Corona and Jesse Thomason and Raymond J. Mooney}, booktitle={Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17)}, month={November}, address={Taipei, Taiwan}, pages={122-127}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127663", year={2017} }

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