Predicting barge-in utterance errors by using implicitly supervised ASR accuracy and barge-in rate per user

Authors:
Kazunori Komatani;Alexander I. Rudnicky
Affiliations:
Kyoto University, Yoshida, Sakyo, Kyoto, Japan;Carnegie Mellon University, Pittsburgh, PA
Venue:
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Year:
2009

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Abstract

Modeling of individual users is a promising way of improving the performance of spoken dialogue systems deployed for the general public and utilized repeatedly. We define "implicitly-supervised" ASR accuracy per user on the basis of responses following the system's explicit confirmations. We combine the estimated ASR accuracy with the user's barge-in rate, which represents how well the user is accustomed to using the system, to predict interpretation errors in barge-in utterances. Experimental results showed that the estimated ASR accuracy improved prediction performance. Since this ASR accuracy and the barge-in rate are obtainable at runtime, they improve prediction performance without the need for manual labeling.