We describe an experiment that investigates whether sub-utterance prosodic features can be used to detect uncertainty at the wordlevel. That is, given an utterance that is classified as uncertain, we want to determine which word or phrase the speaker is uncertain about. We have a corpus of utterances spoken under varying degrees of certainty. Using combinations of sub-utterance prosodic features we train models to predict the level of certainty of an utterance. On a set of utterances that were perceived to be uncertain, we compare the predictions of our models for two candidate target word segmentations: (a) one with the actual word causing uncertainty as the proposed target word, and (b) one with a control word as the proposed target word. Our best model correctly identifies the word causing the uncertainty rather than the control word 91% of the time.Engineering and Applied Science
