Paper: Predicting Emotion In Spoken Dialogue From Multiple Knowledge Sources

ACL ID N04-1026
Title Predicting Emotion In Spoken Dialogue From Multiple Knowledge Sources
Venue Human Language Technologies
Session Main Conference
Year 2004
Authors

We examine the utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues. We rst annotate student turns in our corpus for negative, neutral and positive emotions. We then automatically extract features represent- ing acoustic-prosodic and other linguistic in- formation from the speech signal and associ- ated transcriptions. We compare the results of machine learning experiments using different feature sets to predict the annotated emotions. Our best performing feature set contains both acoustic-prosodic and other types of linguistic features, extracted from both the current turn and a context of previous student turns, and yields a prediction accuracy of 84.75%, which is a 44% relative i...