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dc.contributor.advisorJames R. Glass and I. Lee Hetherington.en_US
dc.contributor.authorWang, Stanley Xinleien_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2010-03-25T15:02:56Z
dc.date.available2010-03-25T15:02:56Z
dc.date.copyright2009en_US
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/53114
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.en_US
dc.descriptionIncludes bibliographical references (p. 87-90).en_US
dc.description.abstractThis research explores applications of joint letter-phoneme subwords, known as graphones, in several domains to enable detection and recognition of previously unknown words. For these experiments, graphones models are integrated into the SUMMIT speech recognition framework. First, graphones are applied to automatically generate pronunciations of restaurant names for a speech recognizer. Word recognition evaluations show that graphones are effective for generating pronunciations for these words. Next, a graphone hybrid recognizer is built and tested for searching song lyrics by voice, as well as transcribing spoken lectures in a open vocabulary scenario. These experiments demonstrate significant improvement over traditional word-only speech recognizers. Modifications to the flat hybrid model such as reducing the graphone set size are also considered. Finally, a hierarchical hybrid model is built and compared with the flat hybrid model on the lecture transcription task.en_US
dc.description.statementofresponsibilityby Stanley Xinlei Wang.en_US
dc.format.extent90 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.subjectElectrical Engineering and Computer Science.en_US
dc.titleUsing graphone models in automatic speech recognitionen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc503114771en_US


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