ISCA Archive Interspeech 2023
ISCA Archive Interspeech 2023

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

Sergio Burdisso, Esaú Villatoro-Tello, Srikanth Madikeri, Petr Motlicek

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.


doi: 10.21437/Interspeech.2023-1923

Cite as: Burdisso, S., Villatoro-Tello, E., Madikeri, S., Motlicek, P. (2023) Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews. Proc. INTERSPEECH 2023, 3617-3621, doi: 10.21437/Interspeech.2023-1923

@inproceedings{burdisso23_interspeech,
  author={Sergio Burdisso and Esaú Villatoro-Tello and Srikanth Madikeri and Petr Motlicek},
  title={{Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
  pages={3617--3621},
  doi={10.21437/Interspeech.2023-1923}
}