Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables

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.