Detail publikace

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

BURDISSO, S. VILLATORO-TELLO, E. MADIKERI, S. MOTLÍČEK, P.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

depression detection, graph neural networks, node weighted graphs, limited training data, interpretability.

Autoři

BURDISSO, S.; VILLATORO-TELLO, E.; MADIKERI, S.; MOTLÍČEK, P.

Vydáno

20. 8. 2023

Nakladatel

International Speech Communication Association

Místo

Dublin

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Ročník

2023

Číslo

8

Stát

Francouzská republika

Strany od

3617

Strany do

3621

Strany počet

5

URL

https://www.isca-archive.org/interspeech_2023/burdisso23_interspeech.pdf

BibTex

@inproceedings{BUT187755,
  author="BURDISSO, S. and VILLATORO-TELLO, E. and MADIKERI, S. and MOTLÍČEK, P.",
  title="Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews",
  booktitle="Proceedings of the Annual Conference of International Speech Communication Association, INTERSPEECH",
  year="2023",
  journal="Proceedings of Interspeech",
  volume="2023",
  number="8",
  pages="3617--3621",
  publisher="International Speech Communication Association",
  address="Dublin",
  doi="10.21437/Interspeech.2023-1923",
  issn="1990-9772",
  url="https://www.isca-archive.org/interspeech_2023/burdisso23_interspeech.pdf"
}