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The Efficiency of Programming Through Automated Speech Recognition

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dc.contributor.advisor Dougherty, John P.
dc.contributor.author English, Matt
dc.date.accessioned 2016-01-19T16:54:42Z
dc.date.available 2016-01-19T16:54:42Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/10066/17627
dc.description.abstract The goal of this thesis is to present information which will support programming through speech recognition by finding a limit for the size of commands in the given language of the program. This limit is the point at which it will take too long for speech to be converted into text by the Viterbi Algorithm, based on the size of a Hidden Markov Model. The paper begins by introducing how speech recognition works, presenting some common issues when attempting to program by voice, and the overall motivation behind the research presented. A history of speech recognition is shown to support how programming by voice has evolved positively over time, allowing users to do more by voice alone. The algorithms which convert speech into text are formally discussed to provide an understanding of their runtimes which will be the main focus of the experiment. The future of speech recognition is also discussed based on speculations of its advancement technologically and based on the results of the experiment itself.
dc.description.sponsorship Haverford College. Department of Computer Science
dc.language.iso eng
dc.rights.uri http://creativecommons.org/licenses/by-nc/3.0/us/
dc.subject.lcsh Automatic speech recognition
dc.subject.lcsh Hidden Markov models
dc.title The Efficiency of Programming Through Automated Speech Recognition
dc.type Thesis
dc.rights.access Open Access


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http://creativecommons.org/licenses/by-nc/3.0/us/ Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/3.0/us/

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