Publication detail

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

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

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

20. 8. 2023

Publisher

International Speech Communication Association

Location

Dublin

ISBN

1990-9772

Periodical

Proceedings of Interspeech

Year of study

2023

Number

8

State

French Republic

Pages from

3617

Pages to

3621

Pages count

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"
}