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
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13–24
- Language:
- URL:
- https://aclanthology.org/W18-0602
- DOI:
- 10.18653/v1/W18-0602
- Cite (ACL):
- Michelle Morales, Stefan Scherer, and Rivka Levitan. 2018. A Linguistically-Informed Fusion Approach for Multimodal Depression Detection. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 13–24, New Orleans, LA. Association for Computational Linguistics.
- Cite (Informal):
- A Linguistically-Informed Fusion Approach for Multimodal Depression Detection (Morales et al., CLPsych 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-0602.pdf