2026-09-19T17:15:13Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/531772022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Patrick H. Winston and Stephanie Shattuck-Hufnagel.
Nti, Akua Afriyie
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:10:51Z
2010-03-25T15:10:51Z
2009
2009
http://hdl.handle.net/1721.1/53177
518080995
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (leaves 65-71).
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.
by Akua Afriyie Nti.
M.Eng.
71 leaves
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.
Studying dialects to understand human language
Thesis
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Publication
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