Towards Continuous Estimation of Dissatisfaction in Spoken Dialog

Abstract

We collected a corpus of human-human taskoriented dialogs rich in dissatisfaction and built a model that used prosodic features to predict when the user was likely dissatisfied. For utterances this attained a F .25 score of 0.62, against a baseline of 0.39. Based on qualitative observations and failure analysis, we discuss likely ways to improve this result to make it have practical utility.