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.
Cite
CITATION STYLE
Morales, M. R., Scherer, S., & Levitan, R. (2018). A linguistically-informed fusion approach for multimodal depression detection. In Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, CLPsych 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018 (pp. 13–24). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-0602
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