  Depression detection using vocal biomarkers is a highly researched area.
Articulatory coordination features (ACFs) are developed based on the changes in
neuromotor coordination due to psychomotor slowing, a key feature of Major
Depressive Disorder. However findings of existing studies are mostly validated
on a single database which limits the generalizability of results. Variability
across different depression databases adversely affects the results in cross
corpus evaluations (CCEs). We propose to develop a generalized classifier for
depression detection using a dilated Convolutional Neural Network which is
trained on ACFs extracted from two depression databases. We show that ACFs
derived from Vocal Tract Variables (TVs) show promise as a robust set of
features for depression detection. Our model achieves relative accuracy
improvements of ~10% compared to CCEs performed on models trained on a single
database. We extend the study to show that fusing TVs and Mel-Frequency
Cepstral Coefficients can further improve the performance of this classifier.
