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Contrasting multi-lingual prosodic cues to predict verbal feedback for rapport

  • University of Washington

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Verbal feedback is an important information source in establishing interactional rapport. However, predicting verbal feedback across languages is challenging due to languagespecific differences, inter-speaker variation, and the relative sparseness and optionality of verbal feedback. In this paper, we employ an approach combining classifier weighting and SMOTE algorithm oversampling to improve verbal feedback prediction in Arabic, English, and Spanish dyadic conversations. This approach improves the prediction of verbal feedback, up to 6-fold, while maintaining a high overall accuracy. Analyzing highly weighted features highlights widespread use of pitch, with more varied use of intensity and duration.

Original languageEnglish
Title of host publicationACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
Pages614-619
Number of pages6
StatePublished - 2011
Event49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 - Portland, OR, United States
Duration: Jun 19 2011Jun 24 2011

Publication series

NameACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Volume2

Conference

Conference49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Country/TerritoryUnited States
CityPortland, OR
Period06/19/1106/24/11

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