Multilingual Speaker-Invariant Dysarthria Severity Assessment Using Adversarial Domain Adaptation and Self-Supervised Learning

Abstract

Traditional assessments for dysarthria are subjective and time-consuming, highlighting the need for automated, objective approaches that can be scaled for remote and resource-constrained environments. This paper introduces an adversarial domain adaptation framework tailored for dysarthria severity assessment, addressing the challenges of high intra-class variability and limited availability of dysarthric speech data. By framing speaker variability as a domain adaptation problem, we utilise an adversarially trained feature extractor to derive speaker-invariant yet discriminatively powerful representations utilising speech features learned through self-supervised learning. Experiments on previously unseen and diverse speakers reveal that the proposed approach yields a 7.86% average improvement across multilingual datasets compared to traditional severity-discriminative training, outperforming competitive baselines by 12.33% on average. Additionally, the method inherently supports privacy-preserving applications by minimising reliance on speaker-specific information. The results demonstrate strong alignment with clinical assessments, reinforcing our model’s clinical relevance and effectiveness.