Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU150720" target="_blank" >RIV/00216305:26230/23:PU150720 - isvavai.cz</a>

Result on the web

<a href="https://www.isca-archive.org/interspeech_2023/burdisso23_interspeech.pdf" target="_blank" >https://www.isca-archive.org/interspeech_2023/burdisso23_interspeech.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2023-1923" target="_blank" >10.21437/Interspeech.2023-1923</a>

Result language

angličtina

Original language name

Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

Original language description

We propose a simple approach for weighting self- connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for model- ing non-consecutive and long-distance semantics to classify the transcriptions into depressed or control subjects. The proposed method aims to mitigate the limiting assumptions of locality and the equal importance of self-connections vs. edges to neighbor- ing nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capa- bilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outper- forms the vanilla GCN model as well as previously reported re- sults, achieving an F1=0.84% on both datasets. Finally, a qual- itative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Annual Conference of International Speech Communication Association, INTERSPEECH

ISBN

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ISSN

1990-9772

e-ISSN

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Number of pages

5

Pages from-to

3617-3621

Publisher name

International Speech Communication Association

Place of publication

Dublin

Event location

Dublin

Event date

Aug 20, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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