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All Subjects: Vowels
Creators: Daliri, Ayoub

Vowel Normalization in Dysarthria

Description

In this study, the Bark transform and Lobanov method were used to normalize vowel formants in speech produced by persons with dysarthria. The computer classification accuracy of these normalized data were then compared to the results of human perceptual classification accuracy of the actual vowels. These results were then analyzed…

ContributorsJones, Hanna Vanessa (Author) / Liss, Julie (Thesis director) / Dorman, Michael (Committee member) / Borrie, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Department of Speech and Hearing Science (Contributor) / Department of English (Contributor) / Speech and Hearing Science (Contributor)

Created2013-05

Learning Rate in Auditory Motor Adaptation

Description

Past studies have shown that auditory feedback plays an important role in maintaining the speech production system. Typically, speakers compensate for auditory feedback alterations when the alterations persist over time (auditory motor adaptation). Our study focused on how to increase the rate of adaptation by using different auditory feedback conditions. For the present study, we recruited a total of 30 participants. We examined auditory motor adaptation after participants completed three conditions: Normal speaking, noise-masked speaking, and silent reading. The normal condition was used as a control condition. In the noise-masked condition, noise was added to the auditory feedback to completely mask speech outputs. In the silent reading condition, participants were instructed to silently read target words in their heads, then read the words out loud. We found that the learning rate in the noise-masked condition was lower than that in the normal condition. In contrast, participants adapted at a faster rate after they experience the silent reading condition. Overall, this study demonstrated that adaptation rate can be modified through pre-exposing participants to different types auditory-motor manipulations.

ContributorsNavarrete, Karina (Author) / Daliri, Ayoub (Thesis director) / Peter, Beate (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)

Created2019-05

Development of a MATLAB Software to Localize the ‘Hotspot’ for TMS Studies

Description

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique used in a variety of research settings, including speech neuroscience studies. However, one of the difficulties in using TMS for speech studies is the time that it takes to localize the lip motor cortex representation on the scalp. For my project, I used MATLAB to create a software package that facilitates the localization of the ‘hotspot’ for TMS studies in a systematic, reliable manner. The software sends TMS pulses at certain locations, collects electromyography (EMG) data, and extracts motor-evoked potentials (MEPs) to help users visualize the resulting muscle activation. In this way, users can systematically find the subject’s hotspot for TMS stimulation of the motor cortex. The hotspot detection software was found to be an effective and efficient improvement on previous localization methods.

ContributorsKshatriya, Nyah (Author) / Daliri, Ayoub (Thesis director) / Liss, Julie (Committee member) / Barrett, The Honors College (Contributor) / Business (Minor) (Contributor)

Created2022-05