Multilingual speaker-invariant dysarthria severity assessment using adversarial domain adaptation and self-supervised learning
File(s)
Accepted version
Author(s)
Stumpf, Lauren
Kadirvelu, Balasundaram
Faisal, Aldo
Type
Conference Paper
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.
Date Issued
2025-03-07
Date Acceptance
2025-02-10
Citation
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025
Publisher
IEEE
Start Page
1
End Page
5
Journal / Book Title
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Copyright Statement
© 2025 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
2025 IEEE International Conference on Acoustics, Speech, and Signal Processing
Publication Status
Published
Start Date
2025-04-06
Finish Date
2025-04-11
Coverage Spatial
Hyderabad, India
Date Publish Online
2025-03-07