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Towards Understanding And Improving Speech Processing, Sonia Yasmin
Towards Understanding And Improving Speech Processing, Sonia Yasmin
Electronic Thesis and Dissertation Repository
This dissertation explores mechanisms for understanding and improving speech processing. First, I used EEG to investigate the acoustic and semantic processing of continuous naturalistic speech masked by multi-talker babble. I found that different features of the same speech signal are reflected in different aspects of the neural tracking response, which are themselves differentially affected by noise. These findings point to a complex relationship between speech intelligibility and neural speech encoding.
Next, I systematically reviewed the current advancements in speech enhancement technologies. I find that speech enhancement algorithms are limited in their generalizability to speech-noise (i.e., babble). I demonstrate that, for …
Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi
Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi
Electronic Thesis and Dissertation Repository
Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques.
To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel …