Using Context to Improve Emotion Detection in Spoken Dialog Systems

jackson liscombe, giuseppe riccardi, Dilek Hakkani-Tur

Abstract:

Most research that explores the emotional state of users of spo-
ken dialog systems does not fully utilize the contextual nature
that the dialog structure provides. This paper reports results of
machine learning experiments designed to automatically clas-
sify the emotional state of user turns using a corpus of 5,690
dialogs collected with the "How May Help You ?" spoken dialog system.
We show that augmenting standard lexical and prosodic features with contextual features
that exploit the struc- ture of spoken dialog and track user state increases classification
accuracy by 2.6%.