Gender-aware estimation of depression severity level in a multimodal setting

Syed Arbaaz Oureshi; Gael Dias; Sriparna Saha; Mohammed Hasanuzzaman

doi:10.1109/IJCNN52387.2021.9534330

Gender-aware estimation of depression severity level in a multimodal setting

Syed Arbaaz Oureshi, Gael Dias, Sriparna Saha, Mohammed Hasanuzzaman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

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.

Original language	English
Title of host publication	2021 International Joint Conference on Neural Networks (IJCNN): Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	8
ISBN (Electronic)	9781665439008
ISBN (Print)	9781665445979
DOIs	https://doi.org/10.1109/IJCNN52387.2021.9534330
Publication status	Published - 21 Sept 2021
Externally published	Yes
Event	2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China Duration: 18 Jul 2021 → 22 Jul 2021

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2021-July
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/Territory	China
City	Virtual, Shenzhen
Period	18/07/2021 → 22/07/2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Depression
Gender
Multitask learning

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN52387.2021.9534330Licence: Unspecified

Cite this

Oureshi, S. A., Dias, G., Saha, S., & Hasanuzzaman, M. (2021). Gender-aware estimation of depression severity level in a multimodal setting. In 2021 International Joint Conference on Neural Networks (IJCNN): Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN52387.2021.9534330

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title = "Gender-aware estimation of depression severity level in a multimodal setting",

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.",

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author = "Oureshi, {Syed Arbaaz} and Gael Dias and Sriparna Saha and Mohammed Hasanuzzaman",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",

year = "2021",

month = sep,

day = "21",

doi = "10.1109/IJCNN52387.2021.9534330",

language = "English",

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Oureshi, SA, Dias, G, Saha, S & Hasanuzzaman, M 2021, Gender-aware estimation of depression severity level in a multimodal setting. in 2021 International Joint Conference on Neural Networks (IJCNN): Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Shenzhen, China, 18/07/2021. https://doi.org/10.1109/IJCNN52387.2021.9534330

Gender-aware estimation of depression severity level in a multimodal setting. / Oureshi, Syed Arbaaz; Dias, Gael; Saha, Sriparna et al.
2021 International Joint Conference on Neural Networks (IJCNN): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Gender-aware estimation of depression severity level in a multimodal setting

AU - Oureshi, Syed Arbaaz

AU - Dias, Gael

AU - Saha, Sriparna

AU - Hasanuzzaman, Mohammed

PY - 2021/9/21

Y1 - 2021/9/21

N2 - 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.

AB - 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.

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KW - Gender

KW - Multitask learning

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DO - 10.1109/IJCNN52387.2021.9534330

M3 - Conference contribution

AN - SCOPUS:85116415077

SN - 9781665445979

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2021 International Joint Conference on Neural Networks (IJCNN): Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021

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ER -

Oureshi SA, Dias G, Saha S, Hasanuzzaman M. Gender-aware estimation of depression severity level in a multimodal setting. In 2021 International Joint Conference on Neural Networks (IJCNN): Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN52387.2021.9534330

Gender-aware estimation of depression severity level in a multimodal setting

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this