An artificial Intelligence Approach to improving Speech Recognition

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dc.contributor.advisorMashao, Danielen_ZA
dc.contributor.advisorVentura, Necoen_ZA
dc.contributor.authorLopes, Luis Ramos dos Santosen_ZA
dc.date.accessioned2014-07-31T10:55:06Z
dc.date.available2014-07-31T10:55:06Z
dc.date.issued2009en_ZA
dc.description.abstractSpeech Recognition is a technology with promising applications. However, the performance of current speech recognizers greatly limit their widespread use. Approaches to reducing the word error rate have mainly been associated with statistical techniques. As a consequence, speech recognition results can still contain sentences that are nonsensical. The method proposed here, is to analize the output of any chosen speech recognition system, in order to determine whether a sentence contains syntactic or semantic errors. This is done via a software agent that uses the information from its knowledge base to attempt to correct the errors found. A system was implemented with a small vocabulary speaker-independent continuous speech recognition system, with limited sentence structures. The achieved increase in speech recognition accuracy, shows that there are bene ts in using this approach.en_ZA
dc.identifier.apacitationLopes, L. R. d. S. (2009). <i>An artificial Intelligence Approach to improving Speech Recognition</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/5180en_ZA
dc.identifier.chicagocitationLopes, Luis Ramos dos Santos. <i>"An artificial Intelligence Approach to improving Speech Recognition."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2009. http://hdl.handle.net/11427/5180en_ZA
dc.identifier.citationLopes, L. 2009. An artificial Intelligence Approach to improving Speech Recognition. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Lopes, Luis Ramos dos Santos AB - Speech Recognition is a technology with promising applications. However, the performance of current speech recognizers greatly limit their widespread use. Approaches to reducing the word error rate have mainly been associated with statistical techniques. As a consequence, speech recognition results can still contain sentences that are nonsensical. The method proposed here, is to analize the output of any chosen speech recognition system, in order to determine whether a sentence contains syntactic or semantic errors. This is done via a software agent that uses the information from its knowledge base to attempt to correct the errors found. A system was implemented with a small vocabulary speaker-independent continuous speech recognition system, with limited sentence structures. The achieved increase in speech recognition accuracy, shows that there are bene ts in using this approach. DA - 2009 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2009 T1 - An artificial Intelligence Approach to improving Speech Recognition TI - An artificial Intelligence Approach to improving Speech Recognition UR - http://hdl.handle.net/11427/5180 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5180
dc.identifier.vancouvercitationLopes LRdS. An artificial Intelligence Approach to improving Speech Recognition. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2009 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5180en_ZA
dc.language.isoeng
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherEngineeringen_ZA
dc.titleAn artificial Intelligence Approach to improving Speech Recognitionen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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