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dc.contributor.advisorAlex P. Pentland.en_US
dc.contributor.authorStoltzman, William Ten_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2008-05-19T14:57:57Z
dc.date.available2008-05-19T14:57:57Z
dc.date.copyright2006en_US
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/41537
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionIncludes bibliographical references (p. 67-70).en_US
dc.description.abstractLanguage is not the only form of verbal communication. Loudness, pitch, speaking rate, and other non-linguistic speech features are crucial aspects of human spoken interaction. In this thesis, we separate these speech features into two categories -- vocal Activity and vocal Emphasis -- and propose a framework for classifying high-level social behavior according to those metrics. We present experiments showing that non-linguistic speech analysis alone can account for appreciable portions of social phenomena. We report statistically significant results in measuring the persuasiveness of pitches, the effectiveness of customer service representatives, and the severity of depression. Effect sizes of these studies explain up to 60% of the sample variances and yield binary decision accuracies nearing 90%.en_US
dc.description.statementofresponsibilityby William T. Stoltzman.en_US
dc.format.extent70 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleToward a social signaling framework : activity and emphasis in speechen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc216884351en_US


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