  Automating dysarthria assessments offers the opportunity to develop
practical, low-cost tools that address the current limitations of manual and
subjective assessments. Nonetheless, the small size of most dysarthria datasets
makes it challenging to develop automated assessment. Recent research showed
that speech representations from models pre-trained on large unlabelled data
can enhance Automatic Speech Recognition (ASR) performance for dysarthric
speech. We are the first to evaluate the representations from pre-trained
state-of-the-art Self-Supervised models across three downstream tasks on
dysarthric speech: disease classification, word recognition and intelligibility
classification, and under three noise scenarios on the UA-Speech dataset. We
show that HuBERT is the most versatile feature extractor across dysarthria
classification, word recognition, and intelligibility classification, achieving
respectively $+24.7\%, +61\%, \text{and} +7.2\%$ accuracy compared to classical
acoustic features.
