Problem
Research questions and friction points this paper is trying to address.
Cross-Lingual
Acoustic Disease-Alignment
Respiratory Health Assessment
Spontaneous Speech
Language-Specific Phonetic Variation
A Cross-Lingual Acoustic Disease-Alignment Framework for Respiratory Health Assessment from Spontaneous Speech
🤖 AI Summary
本文提出CL-DAF框架，通过识别跨语言一致的疾病相关声学特征来解决多语言呼吸健康评估中的模型泛化问题。
📝 Abstract
Spontaneous speech offers a scalable, noninvasive signal for respiratory health assessment, yet interpretable models that generalize across languages remain challenging because disease-related acoustic changes are confounded by language-specific phonetic variation. We present CL-DAF, a Cross-Lingual Disease-Alignment Framework that identifies acoustic dimensions whose disease effects remain consistent across languages. Using 201 English and 75 newly collected Bangla speakers, we construct a common 272-dimensional acoustic representation and quantify disease alignment using signed rank-biserial effects and the Language Invariance Score. We first show that spontaneous Bangla speech separates COPD from controls (AUC 0.85); however, 133 features reverse their disease direction across languages and the full representation transfers poorly (AUC 0.49 from Bangla to English). CL-DAF isolates 26 disease-aligned features that raise AUCs to 0.825 and 0.722 from English to Bangla and Bangla to English, respectively. These findings provide a foundation for multilingual clinical speech models emphasizing pathology over language-dependent variation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cross-Lingual Disease-Alignment Framework
Acoustic Dimensions
Language Invariance Score
Disease-Aligned Features
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