Voice Biomarker Analysis and Automated Severity Classification of Dysarthric Speech in a Multilingual Context

TL;DR

This paper introduces a multilingual approach for classifying dysarthria severity using voice biomarkers across English, Korean, and Tamil, aiming to improve global diagnostic accessibility.

Contribution

It presents a novel multilingual dysarthria severity classification method, expanding beyond monolingual studies to support diverse linguistic populations.

Findings

Effective classification across three languages

Supports equitable access to diagnosis

Advances automated dysarthria assessment

Abstract

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.