A Cross-modal Review of Indicators for Depression Detection Systems

Michelle Morales, Stefan Scherer, Rivka Levitan


Abstract
Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.
Anthology ID:
W17-3101
Volume:
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality
Month:
August
Year:
2017
Address:
Vancouver, BC
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–12
Language:
URL:
https://aclanthology.org/W17-3101
DOI:
10.18653/v1/W17-3101
Bibkey:
Cite (ACL):
Michelle Morales, Stefan Scherer, and Rivka Levitan. 2017. A Cross-modal Review of Indicators for Depression Detection Systems. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality, pages 1–12, Vancouver, BC. Association for Computational Linguistics.
Cite (Informal):
A Cross-modal Review of Indicators for Depression Detection Systems (Morales et al., CLPsych 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-3101.pdf