Publication Details
Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
depression detection, graph neural networks, node weighted graphs, limited training data, interpretability.
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.
@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"
}