logo Idiap Research Institute        
 [BibTeX] [Marc21]
Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
Type of publication: Conference paper
Citation: Burdisso_INTERSPEECH_2023
Publication status: Accepted
Booktitle: Proceedings of Interspeech
Year: 2023
Crossref: Burdisso_Idiap-RR-03-2023:
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 modeling 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 neighboring nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capabilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outperforms the vanilla GCN model as well as previously reported results, achieving an F1=0.84% on both datasets. Finally, a qualitative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.
Projects Idiap
Authors Burdisso, Sergio
Madikeri, Srikanth
Motlicek, Petr
Added by: [UNK]
Total mark: 0
  • Burdisso_INTERSPEECH_2023.pdf