2026-09-22T10:58:18Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1514382023-08-01T03:14:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Shattuck-Hufnagel, Stefanie
Torres, Deborah Cheron
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:39:51Z
2023-07-31T19:39:51Z
2023-06
2023-06-06T16:35:07.525Z
https://hdl.handle.net/1721.1/151438
Speech recognition and analysis can be improved by using methods that can effectively characterize important speech patterns of a speaker without requiring hours of data. This thesis defines a method by which key contexts related to systematic speech modification can be used to create a profile of the speech produced by a speaker. Using acoustic and prosodic information, contexts that create the potential for speech modifications can be specified. Then, by filtering speech produced by a speaker in the targeted contexts, the patterns of speech production in these contexts can be characterized. With these productions, likely underlying contexts that are associated with the productions can be used to enhance speech recognition when these contexts arise in new speech.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
An algorithm for characterizing
context-governed speech production patterns
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Speech recognition and analysis can be improved by using methods that can effectively characterize important speech patterns of a speaker without requiring hours of data. This thesis defines a method by which key contexts related to systematic speech modification can be used to create a profile of the speech produced by a speaker. Using acoustic and prosodic information, contexts that create the potential for speech modifications can be specified. Then, by filtering speech produced by a speaker in the targeted contexts, the patterns of speech production in these contexts can be characterized. With these productions, likely underlying contexts that are associated with the productions can be used to enhance speech recognition when these contexts arise in new speech.</Abstract>
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