  Depression has been the leading cause of mental-health illness worldwide.
Major depressive disorder (MDD), is a common mental health disorder that
affects both psychologically as well as physically which could lead to loss of
lives. Due to the lack of diagnostic tests and subjectivity involved in
detecting depression, there is a growing interest in using behavioural cues to
automate depression diagnosis and stage prediction. The absence of labelled
behavioural datasets for such problems and the huge amount of variations
possible in behaviour makes the problem more challenging. This paper presents a
novel multi-level attention based network for multi-modal depression prediction
that fuses features from audio, video and text modalities while learning the
intra and inter modality relevance. The multi-level attention reinforces
overall learning by selecting the most influential features within each
modality for the decision making. We perform exhaustive experimentation to
create different regression models for audio, video and text modalities.
Several fusions models with different configurations are constructed to
understand the impact of each feature and modality. We outperform the current
baseline by 17.52% in terms of root mean squared error.
