Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources


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 first annotate student turns in our corpus for negative, neutral and positive emotions. We then automatically extract features representing acoustic-prosodic and other linguistic… (More)

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