Improving Black-box Speech Recognition using Semantic Parsing (2017)
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.
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Presentation:
Poster
Rodolfo Corona Undergraduate Student rcorona [at] utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Jesse Thomason Ph.D. Student jesse [at] cs utexas edu