10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Classifying Turn-Level Uncertainty Using Word-Level Prosody

Diane Litman (1), Mihai Rotaru (2), Greg Nicholas (3)

(1) University of Pittsburgh, USA
(2) Textkernel B.V., The Netherlands
(3) Brown University, USA

Spoken dialogue researchers often use supervised machine learning to classify turn-level user affect from a set of turn-level features. The utility of sub-turn features has been less explored, due to the complications introduced by associating a variable number of sub-turn units with a single turn-level classification. We present and evaluate several voting methods for using word-level pitch and energy features to classify turn-level user uncertainty in spoken dialogue data. Our results show that when linguistic knowledge regarding prosody and word position is introduced into a word-level voting model, classification accuracy is significantly improved compared to the use of both turn-level and uninformed word-level models.

Full Paper

Bibliographic reference.  Litman, Diane / Rotaru, Mihai / Nicholas, Greg (2009): "Classifying turn-level uncertainty using word-level prosody", In INTERSPEECH-2009, 2003-2006.