2026-09-18T22:33:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/698052022-01-13T07:55:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Deb Roy.
Miller, Matthew Adam
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2012-03-16T16:04:40Z
2012-03-16T16:04:40Z
2011
2011
http://hdl.handle.net/1721.1/69805
777966321
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 149-152).
The Human Speechome Project is an unprecedented attempt to record, analyze and understand the process of language acquisition. It is composed of over 90,000 hours of video and 150,000 hours of audio, capturing roughly 80% of the waking hours of a single child from his birth until age 3. This thesis proposes and develops a method for representing and analyzing a video corpus of this scale that is both compact and efficient, while retaining much of the important information about large scale behaviors of the recorded subjects. This representation is shown to be useful for the unsupervised modeling, clustering and exploration of the data, particularly when it is combined with text transcripts of the speech. Novel methods are introduced to perform Spatial Latent Semantic Analysis - extending the popular framework for topic modeling to cover behavior as well. Finally, the representation is used to analyze the inherent "spatiality" of individual words. A surprising connection is demonstrated between the uniqueness of a word's spatial distribution and how early it is learned by the child.
by Matthew Miller.
S.M.
152 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
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http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program in Media Arts and Sciences.
Semantic spaces : behavior, language and word learning in the Human Speechome corpus
Thesis
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<Abstract>The Human Speechome Project is an unprecedented attempt to record, analyze and understand the process of language acquisition. It is composed of over 90,000 hours of video and 150,000 hours of audio, capturing roughly 80% of the waking hours of a single child from his birth until age 3. This thesis proposes and develops a method for representing and analyzing a video corpus of this scale that is both compact and efficient, while retaining much of the important information about large scale behaviors of the recorded subjects. This representation is shown to be useful for the unsupervised modeling, clustering and exploration of the data, particularly when it is combined with text transcripts of the speech. Novel methods are introduced to perform Spatial Latent Semantic Analysis - extending the popular framework for topic modeling to cover behavior as well. Finally, the representation is used to analyze the inherent "spatiality" of individual words. A surprising connection is demonstrated between the uniqueness of a word's spatial distribution and how early it is learned by the child.</Abstract>
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