Voice Biomarker Analysis and Automated Severity Classification of Dysarthric Speech in a Multilingual Context
This addresses the need for equitable and efficient diagnosis of dysarthria, a motor speech disorder, for clinicians and patients globally, though it appears incremental by extending existing methods to multiple languages.
The paper tackled the problem of automatically assessing dysarthria severity by proposing a multilingual classification method using English, Korean, and Tamil speech data, aiming to improve accuracy and accessibility beyond monolingual approaches.
Dysarthria, a motor speech disorder, severely impacts voice quality, pronunciation, and prosody, leading to diminished speech intelligibility and reduced quality of life. Accurate assessment is crucial for effective treatment, but traditional perceptual assessments are limited by their subjectivity and resource intensity. To mitigate the limitations, automatic dysarthric speech assessment methods have been proposed to support clinicians on their decision-making. While these methods have shown promising results, most research has focused on monolingual environments. However, multilingual approaches are necessary to address the global burden of dysarthria and ensure equitable access to accurate diagnosis. This thesis proposes a novel multilingual dysarthria severity classification method, by analyzing three languages: English, Korean, and Tamil.