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Studying dialects to understand human language

Author(s)
Nti, Akua Afriyie
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Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Patrick H. Winston and Stephanie Shattuck-Hufnagel.
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M.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. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
This thesis investigates the study of dialect variations as a way to understand how humans might process speech. It evaluates some of the important research in dialect identification and draws conclusions about how their results can give insights into human speech processing. A study clustering dialects using k-means clustering is done. Self-organizing maps are proposed as a tool for dialect research, and a self-organizing map is implemented for the purposes of testing this. Several areas for further research are identified, including how dialects are stored in the brain, more detailed descriptions of how dialects vary, including contextual effects, and more sophisticated visualization tools. Keywords: dialect, accent, identification, recognition, self-organizing maps, words, lexical sets, clustering.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
 
Includes bibliographical references (leaves 65-71).
 
Date issued
2009
URI
http://hdl.handle.net/1721.1/53177
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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