TAILIEUCHUNG - Báo cáo: Recognizing Uncertainty in Speech

Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011, Article ID 251753, 11 pages doi: Research Article Recognizing Uncertainty in Speech Heather Pon-Barry and Stuart M. Shieber School of Engineering and Applied Sciences, Harvard University, 33 Oxford Street, Cambridge, MA 02138, USA Correspondence should be addressed to Heather Pon-Barry, ponbarry@ Received 1 August 2010; Accepted 23 November 2010 Academic Editor: R. Cowie Copyright © 2011 H. Pon-Barry and S. M. Shieber. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly. | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 251753 11 pages doi 2011 251753 Research Article Recognizing Uncertainty in Speech Heather Pon-Barry and Stuart M. Shieber School of Engineering and Applied Sciences Harvard University 33 Oxford Street Cambridge MA 02138 USA Correspondence should be addressed to Heather Pon-Barry ponbarry@ Received 1 August 2010 Accepted 23 November 2010 Academic Editor R. Cowie Copyright 2011 H. Pon-Barry and S. M. Shieber. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We address the problem of inferring a speaker s level of certainty based on prosodic information in the speech signal which has application in speech-based dialogue systems. We show that using phrase-level prosodic features centered around the phrases causing uncertainty in addition to utterance-level prosodic features improves our model s level of certainty classification. In addition our models can be used to predict which phrase a person is uncertain about. These results rely on a novel method for eliciting utterances of varying levels of certainty that allows us to compare the utility of contextually-based feature sets. We elicit level of certainty ratings from both the speakers themselves and a panel of listeners finding that there is often a mismatch between speakers internal states and their perceived states and highlighting the importance of this distinction. 1. Introduction Speech-based technology has become a familiar part of our everyday lives. Yet while most people can think of an instance where they have interacted with a call-center dialogue system or command-based smartphone application few would argue that the experience was as natural or as efficient as conversing with another human. To build computer systems .

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