跨语言分析三种语言的构音障碍语音，实现自动严重程度分类
Voice Biomarker Analysis and Automated Severity Classification of Dysarthric Speech in a Multilingual Context
- 构建多语言语音分析框架，覆盖英、韩、泰三语
- 首次在多语言场景下实现构音障碍严重度自动分类
- 适合语音病理学研究与多语言医疗AI开发人员
构音障碍是一种运动性言语障碍，严重影响语音质量、发音和语调，导致言语可理解性下降及生活质量降低。准确评估对有效治疗至关重要，但传统感知评估受限于主观性和资源消耗。为克服这些局限，已有自动构音障碍语音评估方法被提出以辅助临床决策。尽管这些方法已展现出良好效果，但多数研究集中于单语言环境。然而，要应对构音障碍的全球负担并确保诊断的公平可及性，多语言方法不可或缺。本论文提出一种新型多语言构音障碍严重程度分类方法，分析英语、韩语和泰语三种语言的语音数据。
原文摘要 · Abstract (English)
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.
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