Multimodal Depression Detection: An Investigation of Features and Fusion Techniques for Automated Systems

Author

Michelle Renee Morales, The Graduate Center, City University of New YorkFollow

Date of Degree

5-2018

Document Type

Dissertation

Degree Name

Ph.D.

Program

Linguistics

Advisor

Rivka Levitan

Committee Members

Martin Chodorow

Andrew Rosenberg

Stefan Scherer

Subject Categories

Artificial Intelligence and Robotics | Clinical Psychology | Computational Linguistics

Keywords

Depression, Multimodal, Natural Language Processing, Machine Learning, Feature Engineering, Fusion

Abstract

Depression is a serious illness that affects a large portion of the world’s population. Given the large effect it has on society, it is evident that depression is a serious health issue. This thesis evaluates, at length, how technology may aid in assessing depression. We present an in-depth investigation of features and fusion techniques for depression detection systems. We also present OpenMM: a novel tool for multimodal feature extraction. Lastly, we present novel techniques for multimodal fusion. The contributions of this work add considerably to our knowledge of depression detection systems and have the potential to improve future systems by incorporating that knowledge into their design.

Recommended Citation

Morales, Michelle Renee, "Multimodal Depression Detection: An Investigation of Features and Fusion Techniques for Automated Systems" (2018). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/2560