Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/11255
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Watter, Scott | en_US |
dc.contributor.advisor | Humphreys, Karin | en_US |
dc.contributor.advisor | Yoon, Tae-Jin | en_US |
dc.contributor.author | Horbatiuk, Ian | en_US |
dc.date.accessioned | 2014-06-18T16:54:05Z | - |
dc.date.available | 2014-06-18T16:54:05Z | - |
dc.date.created | 2011-09-24 | en_US |
dc.date.issued | 2011-10 | en_US |
dc.identifier.other | opendissertations/6236 | en_US |
dc.identifier.other | 7265 | en_US |
dc.identifier.other | 2256364 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/11255 | - |
dc.description.abstract | <p>Localization of speech onsets to determine onset latencies is a complicated problem with as many different methods for finding them as there are different areas which use such measurements. A majority of research performed in cognition uses a standard amplitude threshold voice key for estimating the speech onset latencies but a number of studies have shown that this method is incredibly inaccurate and can bias data or produce contradictory results. A number of alternative methods based on modifications to traditional voice-keys have been proposed to deal with the inconsistency although still show a number of deficiencies. Previous work has suggested that switching from the amplitude domain of a signal to the frequency domain a number of the issues present with voice keys can be overcome and when used in conjunction with a number of highly sensitive heuristics highly accurate onset latencies can be produced reliably under ideal conditions. This research is refined and paired with a new user interface to improve the ease of use and increase the adoption rate of this type of analysis. Recent work in the telecommunications industry also suggests that wavelet-based algorithms in conjunction with the Teager Energy Operator (TEO) can accurately detect speech even in the presence of noise. Four wavelet-based methods are investigated and tested; a simple wavelet transform test, and three methods using wavelet-packet transforms in conjunction with the TEO. Although these methods do not perform very well compared to traditional methods a number of potential issues with the implementation are discussed.</p> | en_US |
dc.subject | Fourier | en_US |
dc.subject | Wavelet | en_US |
dc.subject | Speech | en_US |
dc.subject | Onset | en_US |
dc.subject | Latencies | en_US |
dc.subject | VAD | en_US |
dc.subject | Other Computer Sciences | en_US |
dc.subject | Psychology | en_US |
dc.subject | Quantitative Psychology | en_US |
dc.subject | Other Computer Sciences | en_US |
dc.title | Fourier & Wavelet Methods for Finding Speech Onset Latencies | en_US |
dc.type | thesis | en_US |
dc.contributor.department | Psychology | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
---|---|---|---|
fulltext.pdf | 524.81 kB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.