Speaker-Independent Dysarthria Severity Classification using
  Self-Supervised Transformers and Multi-Task Learning
Lauren Stumpf Balasundaram Kadirvelu Sigourney Waibel A. Aldo Faisal
Dysarthria, a condition resulting from impaired control of the speech muscles
due to neurological disorders, significantly impacts the communication and
quality of life of patients. The condition's complexity, human scoring and
varied presentations make its assessment and management challenging. This study
presents a transformer-based framework for automatically assessing dysarthria
severity from raw speech data. It can offer an objective, repeatable,
accessible, standardised and cost-effective and compared to traditional methods
requiring human expert assessors. We develop a transformer framework, called
Speaker-Agnostic Latent Regularisation (SALR), incorporating a multi-task
learning objective and contrastive learning for speaker-independent multi-class
dysarthria severity classification. The multi-task framework is designed to
reduce reliance on speaker-specific characteristics and address the intrinsic
intra-class variability of dysarthric speech. We evaluated on the Universal
Access Speech dataset using leave-one-speaker-out cross-validation, our model
demonstrated superior performance over traditional machine learning approaches,
with an accuracy of $70.48\%$ and an F1 score of $59.23\%$. Our SALR model also
exceeded the previous benchmark for AI-based classification, which used support
vector machines, by $16.58\%$. We open the black box of our model by
visualising the latent space where we can observe how the model substantially
reduces speaker-specific cues and amplifies task-specific ones, thereby showing
its robustness. In conclusion, SALR establishes a new benchmark in
speaker-independent multi-class dysarthria severity classification using
generative AI. The potential implications of our findings for broader clinical
applications in automated dysarthria severity assessments.