Deperssion Detection in Naturalistic Environmental Condition

Main Article Content

T R Dineshkumar
U Savitha
T. Haritha vellam
R. Krishika
A.Juneha jabeen
Aravind Ramesh
Purushothaman

Keywords

depression, naturalistic environments

Abstract

In light of depression's massive and growing burden on modern society, researchers are investigating ways to detect depression early using non-invasive, automated, scalable, and non-invasive methods. In naturalistic environments, though, speech-based methods are still needed for effective articulatory information capture. The intermodality and intramodality affinities are learned by integrating information from audio, video, and text modalities in a multimodal depression prediction model this article. As a result of The most important components of each modality in the multilevel attention are selected to be used for decision-making to reinforce the overall learning. Our study aims to build various regression models using inputs from audio, video, and text landmark duration and historic n-gram features on the DAIC _WOZ and SH2 datasets both performed well, either separately or in combination.

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