Name
Abstract | ||
|---|---|---|
Depression is a severe psychological disorder that is experienced by a significant number of individuals across the globe. It greatly changes the way one thinks, triggering a constant decline in mood. Studies have shown that gender can act as a good indicator of depression. In this paper, we analyse the effects of gender information in the estimation of depression. We have carried out different experiments on the benchmark data set named Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). Concretely, we discovered that a) gender information substantially improves the performance of depression severity estimation, and b) adversarially learning to predict the depression score distributed by gender improves the performance of depression severity estimation. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534330 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Depression, Multitask learning, Gender | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Syed Arbaaz Qureshi | 1 | 0 | 0.34 |
| Gaël Dias | 2 | 354 | 41.95 |
| Sriparna Saha | 3 | 1064 | 106.07 |
| Mohammed Hasanuzzaman | 4 | 52 | 13.52 |