2026-09-19T13:10:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/415372022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Alex P. Pentland.
Stoltzman, William T
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-05-19T14:57:57Z
2008-05-19T14:57:57Z
2006
2006
http://hdl.handle.net/1721.1/41537
216884351
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 67-70).
Language 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%.
by William T. Stoltzman.
M.Eng.
70 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Toward a social signaling framework : activity and emphasis in speech
Thesis
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<Abstract>Language 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%.</Abstract>
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