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dc.contributor.advisorDeb Roy.en_US
dc.contributor.authorShaw, George Macaulayen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.en_US
dc.date.accessioned2012-03-16T16:04:54Z
dc.date.available2012-03-16T16:04:54Z
dc.date.copyright2011en_US
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/69808
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 161-163).en_US
dc.description.abstractThis thesis develops a multi-modal dataset consisting of transcribed speech along with the locations in which that speech took place. Speech with location attached is called situated language, and is represented here as spatial distributions, or two-dimensional histograms over locations in a home. These histograms are organized in the form of a taxonomy, where one can explore, compare, and contrast various slices along several axes of interest. This dataset is derived from raw data collected as part of the Human Speechome Project, and consists of semi-automatically transcribed spoken language and time-aligned overhead video collected over 15 months in a typical home environment. As part of this thesis, the vocabulary of the child before the age of two is derived from transcription, as well as the age at which the child first produced each of the 658 words in his vocabulary. Locations are derived using an efficient tracking algorithm, developed as part of this thesis, called 2C. This system maintains high accuracy when compared to similar systems, while dramatically reducing processing time, an essential feature when processing a corpus of this size. Spatial distributions are produced for many different cuts through the data, including temporal segments (i.e. morning, day, and night), speaker identities (i.e. mother, father, child), and linguistic content (i.e. per-word, aggregate by word type). Several visualization types and statistics are developed, which prove useful for organizing and exploring the dataset. It will then be shown that spatial distributions contain a wealth of information, and that this information can be exploited in various ways to derive meaningful insights and numerical results from the data.en_US
dc.description.statementofresponsibilityby George Macaulay Shaw.en_US
dc.format.extent163 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.subjectArchitecture. Program in Media Arts and Sciences.en_US
dc.titleA taxonomy of situated language in natural contextsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.en_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)
dc.identifier.oclc778070000en_US


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