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ACL
2009

Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User

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Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User
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.
Kazunori Komatani, Alexander I. Rudnicky
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Kazunori Komatani, Alexander I. Rudnicky
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