Multi-modal deep learning system for depression and anxiety detection
This work addresses mental health screening for individuals by offering a digital tool, but it is incremental as it builds on existing methods with feature augmentation.
The paper tackled the problem of detecting depression and anxiety by proposing a multi-modal system that integrates deep-learned features from audio and text with hand-crafted features, improving classification F1 scores from 0.58 to 0.63 for depression and from 0.54 to 0.57 for anxiety.
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.