  Traditional screening practices for anxiety and depression pose an impediment
to monitoring and treating these conditions effectively. However, recent
advances in NLP and speech modelling allow textual, acoustic, and hand-crafted
language-based features to jointly form the basis of future mental health
screening and condition detection. Speech is a rich and readily available
source of insight into an individual's cognitive state and by leveraging
different aspects of speech, we can develop new digital biomarkers for
depression and anxiety. To this end, we propose a multi-modal system for the
screening of depression and anxiety from self-administered speech tasks. The
proposed model integrates deep-learned features from audio and text, as well as
hand-crafted features that are informed by clinically-validated domain
knowledge. We find that augmenting hand-crafted features with deep-learned
features improves our overall classification F1 score comparing to a baseline
of hand-crafted features alone from 0.58 to 0.63 for depression and from 0.54
to 0.57 for anxiety. The findings of our work suggest that speech-based
biomarkers for depression and anxiety hold significant promise in the future of
digital health.
