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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Intra-speaker Voice Quality Recognition for Voice Therapy
Committee:
Dr. David Anderson, ECE, Chair, Advisor
Dr. Elliot Moore, ECE
Dr. Christopher Rozell, ECE
Dr. Omer Inan, ECE
Dr. Eva van Leer, GSU
Abstract: A critical problem in voice therapy is poor extra-clinical adherence, stemming mainly from patients’ difficulty replicating and implementing their prescribed voice technique out- side of the therapy session. While clinicians can judge whether a patient’s voice quality resembles the individualized therapeutic target or not, patients have difficulty making this judgment themselves. The goal of therapy—replacing habitual voice production mechanics with optimal ones—cannot be achieved when patients cannot independently replicate the target voice technique and consistently differentiate it from their habitual voice production while speaking. Tools to help patients are lacking, demonstrating a substantial knowledge gap in clinical voice science. Machine learning methods have the potential to learn an in- dividual’s habitual and target voice qualities and subsequently classify future recordings accordingly. Classification results could serve as patient feedback in the clinician’s ab- sence. However, machine learning methods have primarily been applied to differentiate voice disorders or distinguish individual speakers from each other rather than identify in- dividual voice quality variations within a speaker. Therefore, this thesis aims to develop a tool that differentiates patients’ habitual voice quality from their target voice, thereby automating and extending the clinician’s judgment to the extra-clinical setting.
