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

• URL: http://arxiv.org/abs/2011.06739v3
• Date: Fri, 9 Apr 2021 04:29:46 GMT
• Title: Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables
• Authors: Nadee Seneviratne, Carol Espy-Wilson
• Abstract summary: Depression detection using vocal biomarkers is a highly researched area. Findings of existing studies are mostly validated on a single database which limits the generalizability of results. We propose to develop a generalized classifier for depression detection using a dilated Coniculaal Neural Network.
• Score: 4.050982413149992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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