Home > Events > PhD Defense - Nikhil Yadav

PhD Defense - Nikhil Yadav

Start: 7/10/2015 at 2:00PM
End: 7/10/2015 at 5:00PM
Location: 258 Fitzpatrick hall
Attendees: Faculty and students are welcome to attend the presentation portion of the defense. Light refreshments and snacks will be available.
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Nikhil Yadav

July 10, 2015          2:00 pm          258 Fitzpatrick Hall

Dissertation Defense

Advisor:  Dr. Christian Poellabauer

Committee Members:

Dr. Patrick Flynn      Dr. Alan Huebner     Dr. Sandra Schneider 



 Neurological disorders and disease have been known to impact the voice and speech production of affected individuals. Speech signal analysis can provide clinical information that can be used to predict the onset of certain diseases and their progression, together with the effectiveness of treatment procedures. While voice and speech analysis has tremendous potential as the foundation for a new generation of diagnostic tools, the development and deployment of such tools has been hindered by two closely tied problems: (1) the lack of an in-depth understanding of the relationship between neurological disorders and speech production and (2) the small and incomplete sets of voice samples (and the lack of medical context) prior studies are based on. As a consequence, the exact links between neurological conditions and speech are not understood well enough to allow us to design accurate diagnostic tools yet. 

 While, pervasive and mobile technologies have made it easy to collect significant amounts of data that can be used for diagnosis, the collected data are often insufficient to perform appropriate assessment without access to historical data and careful correlation and analysis of data over extended periods of time.

 In this thesis, a portable speech collection and analysis system is designed and implemented to extract acoustic metrics from speech tests developed to study various vocal acoustic biomarkers of speech production affected by concussions. Data is collected and analyzed from high school and collegiate athletes for the Fall 2014 athletic season using this system. Statistical analysis and machine learning techniques are used to predict the most significant metrics that could be indicative of concussions. The system is extensible to incorporate test designs that could be used to study the impact of other neurological conditions and diseases on speech.

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