Analysis of user behavior under error conditions in spoken dialogs

Abstract

We focus on developing an account of user behavior under error conditions, working with annotated data from real human-machine mixed initiative dialogs. In particular, we examine categories of error perception, user behavior under error, effect of user strategies on error recovery, and the role of user initiative in error situations. A conditional probability model smoothed by weighted ASR error rate is proposed. Results show that users discovering errors through implicit confirmations are less likely to get back on track (or succeed) and take a longer time in doing so than other forms of error discovery such as system reject and reprompts. Further successful user error-recovery strategies included more rephrasing, less contradicting, and a tendency to terminate error episodes (cancel and startover) than to attempt at repairing a chain of errors.