| dc.contributor.advisor | Daugherity, Walter C. | |
| dc.creator | Armstrong, Bryan | |
| dc.date.accessioned | 2022-04-01T15:59:52Z | |
| dc.date.available | 2022-04-01T15:59:52Z | |
| dc.date.issued | 1995 | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/CAPSTONE-CharlesD_1979 | |
| dc.description | Program year: 1994/1995 | en |
| dc.description | Digitized from print original stored in HDR | en |
| dc.description.abstract | This report describes a small-scale, isolated word voice recognition module. I explain my approach to the problem of voice recognition and provide performance figures and sample runs to give a picture of how this module operates. In general the module is successful in matching isolated words. However, my project goal of speaker independence was not met.
Voice recognition is not a new idea. Successful voice recognition was achieved in the 1950's using analog speech storage and comparison methods. However, most research focuses on recognition of large vocabularies and continuous speech. For my recognition module, I use a simple matching algorithm that matches waveform data processed by a Fast Fourier Transform to previously stored patterns to recognize human speech. | en |
| dc.format.extent | 66 pages | en |
| dc.format.medium | electronic | en |
| dc.format.mimetype | application/pdf | |
| dc.subject | voice recognition | en |
| dc.subject | isolated words | en |
| dc.subject | performance figures | en |
| dc.subject | Fast Fourier Transform | en |
| dc.subject | recognition module | en |
| dc.title | Small Scale Voice Recognition | en |
| dc.type | Thesis | en |
| thesis.degree.department | Computer Science | en |
| thesis.degree.grantor | University Undergraduate Research Fellow | en |
| thesis.degree.level | Undergraduate | en |
| dc.type.material | text | en |
