A Cross-modal Review of Indicators for Depression Detection Systems

Michelle Morales, Stefan Scherer, Rivka Levitan

[How to correct problems with metadata yourself]


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
Editors:
Kristy Hollingshead, Molly E. Ireland, Kate Loveys
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://preview.aclanthology.org/teach-a-man-to-fish/W17-3101.pdf