Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment

• URL: http://arxiv.org/abs/2606.21306v1
• Date: Fri, 19 Jun 2026 10:38:00 GMT
• Title: Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment
• Abstract summary: Dysarthria severity assessment is essential for therapy planning and longitudinal monitoring, yet manual perceptual rating is time-consuming and variable across clinicians.<n>We propose an influence-based, instance-level explainability framework that explains each decision through supportive and competing training samples.
• Score: 15.648988438396614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dysarthria severity assessment is essential for therapy planning and longitudinal monitoring, yet manual perceptual rating is time-consuming and variable across clinicians. Although deep learning models achieve strong performance, their black-box nature limits clinical adoption. Existing speech explainability methods typically provide acoustic feature importance scores that are difficult for end-users to interpret. We propose an influence-based, instance-level explainability framework that explains each decision through supportive and competing training samples. Using gradient-based influence approximations, we compute per-utterance influence scores to identify supportive and competing training samples for each prediction. Controlled deletion experiments from 5 to 20 percent validate the explanations, showing that removing highly influential samples systematically shifts predictions. This approach provides auditable explanations by linking decisions to perceptible reference cases.

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