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Recognizing Uncertainty in Speech

Abstract

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.

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Correspondence to Heather Pon-Barry.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Pon-Barry, H., Shieber, S.M. Recognizing Uncertainty in Speech. EURASIP J. Adv. Signal Process. 2011, 251753 (2011). https://doi.org/10.1155/2011/251753

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  • DOI: https://doi.org/10.1155/2011/251753

Keywords

  • Information Technology
  • Quantum Information
  • Internal State
  • Speech Signal
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