A Linguistically-Informed Fusion Approach for Multimodal Depression Detection

Michelle Morales, Stefan Scherer, Rivka Levitan


Abstract
Automated depression detection is inherently a multimodal problem. Therefore, it is critical that researchers investigate fusion techniques for multimodal design. This paper presents the first-ever comprehensive study of fusion techniques for depression detection. In addition, we present novel linguistically-motivated fusion techniques, which we find outperform existing approaches.
Anthology ID:
W18-0602
Volume:
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
Month:
June
Year:
2018
Address:
New Orleans, LA
Venues:
CLPsych | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–24
Language:
URL:
https://www.aclweb.org/anthology/W18-0602
DOI:
10.18653/v1/W18-0602
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0602.pdf