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.