  Dysarthric speech detection (DSD) systems aim to detect characteristics of
the neuromotor disorder from speech. Such systems are particularly susceptible
to domain mismatch where the training and testing data come from the source and
target domains respectively, but the two domains may differ in terms of speech
stimuli, disease etiology, etc. It is hard to acquire labelled data in the
target domain, due to high costs of annotating sizeable datasets. This paper
makes a first attempt to formulate cross-domain DSD as an unsupervised domain
adaptation (UDA) problem. We use labelled source-domain data and unlabelled
target-domain data, and propose a multi-task learning strategy, including
dysarthria presence classification (DPC), domain adversarial training (DAT) and
mutual information minimization (MIM), which aim to learn
dysarthria-discriminative and domain-invariant biomarker embeddings.
Specifically, DPC helps biomarker embeddings capture critical indicators of
dysarthria; DAT forces biomarker embeddings to be indistinguishable in source
and target domains; and MIM further reduces the correlation between biomarker
embeddings and domain-related cues. By treating the UASPEECH and TORGO corpora
respectively as the source and target domains, experiments show that the
incorporation of UDA attains absolute increases of 22.2% and 20.0% respectively
in utterance-level weighted average recall and speaker-level accuracy.
