Identifying user corrections automatically in spoken dialogue systems

Authors:
Julia Hirschberg;Diane Litman;Marc Swerts
Affiliations:
AT&T Labs---Research, Florham Park, NJ;AT&T Labs---Research, Florham Park, NJ;IPO, Eindhoven, The Netherlands
Venue:
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Year:
2001

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Abstract

We present results of machine learning experiments designed to identify user corrections of speech recognition errors in a corpus collected from a train information spoken dialogue system. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best performing features reduce classification error from baselines of 25.70-28.99% to 15.72%.