Paper: Predicting User Reactions To System Error

ACL ID P01-1048
Title Predicting User Reactions To System Error
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2001

This paper focuses on the analysis and prediction of so-called aware sites, defined as turns where a user of a spoken dialogue system first becomes aware that the system has made a speech recognition error. We describe statistical comparisons of features of these aware sites in a train timetable spoken dialogue corpus, which re- veal significant prosodic differences between such turns, compared with turns that ‘correct’ speech recogni- tion errors as well as with ‘normal’ turns that are neither aware sites nor corrections. We then present machine learning results in which we show how prosodic features in combination with other automatically available features can predict whether or not a user turn was a normal turn, a correction, and/or an aware site.