Automatic Dysarthria Severity Assessment using Whisper-extracted Features

Evaluating ML architectures for dysarthria severity assessment on TORGO and MSDM

Bachelor thesis (2024)

Authors

C. Charlesworth Electrical Engineering, Mathematics and Computer Science

Contributors

Z. Yue (mentor)
Y. Zhang (mentor)
T. Durieux Software Engineering - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
Dysarthria Dysarthria Detection TORGO Dysarthria severity assessment
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Published Date

27-06-2024

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Dysarthria is a speech disorder commonly caused by neurological disorders such as strokes, cerebral palsy and Amyotrophic Lateral Sclerosis (ALS). The severity level of dysarthria greatly influences the appropriate treatment for a patient. However, assessing the severity of dysarthria in a patient is a time-consuming process that requires a trained speech therapist. Therefore the following work explores a variety of classifier architectures for automatic dysarthria severity assessment using Whisper encodings. The datasets used were MSDM and TORGO while the classifier architectures implemented included a Convolutional Neural Networks and Recurrent Neural Network variants. Across both datasets, the Gated Recurrent Unit network (GRU) achieved the best performance with 97.21% accuracy on MSDM and 97.47% on TORGO.

Files

RP_Final2.pdf
(.pdf | 1.15 Mb)