Human-Machine Problem Solving Using Spoken Language Systems (SLS): Factors Affecting Performance and User Satisfaction

Abstract

We have analyzed three factors affecting user satisfaction and system performance using an SLS implemented in the ATIS domain. We have found that: (1) trade-offs between speed and accuracy have different implications for user satisfaction; (2) recognition performance improves over time, at least in part because of a reduction in sentence perplexity; and (3) hyperarticulation increases recognition errors, and while instructions can reduce this behavior, they do not result in improved recognition performance. We conclude that while users may adapt to some aspects of an SLS, certain types of user behavior may require technological solutions.