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arXiv · 2606.22910

Cross-lingual Retrieval-Augmented Classification for Dysarthria Severity Assessment

Taeyoung Jeong·Insung Lee·Du-Seong Chang·Myoung-Wan Koo
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Abstract

Automatic dysarthria severity assessment is limited by the scarcity of labeled pathological speech data. To address this, we propose Cross-lingual Retrieval-Augmented Classification (CRAC), which leverages speech from a different language via an align-retrieve-fuse pipeline. Supervised contrastive learning first shapes a severity-focused embedding space, then a vector database is built from the opposite-language corpus. During both training and inference, the classifier retrieves top-k references from the aligned space and fuses them with the input via cross-attention. Evaluated on Korean post-stroke and Italian ALS dysarthria datasets under a speaker-independent three-class protocol, CRAC achieves balanced accuracies of 87.3% on Korean and 86.7% on Italian, improving over monolingual baselines by 8.4 and 20.0 percentage points, respectively.

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Taeyoung Jeong, Insung Lee, Du-Seong Chang, Myoung-Wan Koo. 2026-06-22. Cross-lingual Retrieval-Augmented Classification for Dysarthria Severity Assessment. https://arxiv.org/abs/2606.22910

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