  Speech based depression classification has gained immense popularity over the
recent years. However, most of the classification studies have focused on
binary classification to distinguish depressed subjects from non-depressed
subjects. In this paper, we formulate the depression classification task as a
severity level classification problem to provide more granularity to the
classification outcomes. We use articulatory coordination features (ACFs)
developed to capture the changes of neuromotor coordination that happens as a
result of psychomotor slowing, a necessary feature of Major Depressive
Disorder. The ACFs derived from the vocal tract variables (TVs) are used to
train a dilated Convolutional Neural Network based depression classification
model to obtain segment-level predictions. Then, we propose a Recurrent Neural
Network based approach to obtain session-level predictions from segment-level
predictions. We show that strengths of the segment-wise classifier are
amplified when a session-wise classifier is trained on embeddings obtained from
it. The model trained on ACFs derived from TVs show relative improvement of
27.47% in Unweighted Average Recall (UAR) at the session-level classification
task, compared to the ACFs derived from Mel Frequency Cepstral Coefficients
(MFCCs).
