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Classifying turn-level uncertainty using word-level prosody

Litman, D and Rotaru, M and Nicholas, G (2009) Classifying turn-level uncertainty using word-level prosody. In: UNSPECIFIED.

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Abstract

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. Copyright © 2009 ISCA.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Litman, Ddlitman@pitt.eduDLITMAN
Rotaru, M
Nicholas, G
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 26 November 2009
Date Type: Publication
Journal or Publication Title: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Page Range: 2003 - 2006
Event Type: Conference
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
Date Deposited: 18 Dec 2014 15:50
Last Modified: 02 Feb 2019 15:59
URI: http://d-scholarship.pitt.edu/id/eprint/23164

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