  Automatic assessment of dysarthric speech is essential for sustained
treatments and rehabilitation. However, obtaining atypical speech is
challenging, often leading to data scarcity issues. To tackle the problem, we
propose a novel automatic severity assessment method for dysarthric speech,
using the self-supervised model in conjunction with multi-task learning.
Wav2vec 2.0 XLS-R is jointly trained for two different tasks: severity
classification and auxiliary automatic speech recognition (ASR). For the
baseline experiments, we employ hand-crafted acoustic features and machine
learning classifiers such as SVM, MLP, and XGBoost. Explored on the Korean
dysarthric speech QoLT database, our model outperforms the traditional baseline
methods, with a relative percentage increase of 1.25% for F1-score. In
addition, the proposed model surpasses the model trained without ASR head,
achieving 10.61% relative percentage improvements. Furthermore, we present how
multi-task learning affects the severity classification performance by
analyzing the latent representations and regularization effect.
