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Classification of Depression and Its Severity Based on Multiple Audio Features Using a Graphical Convolutional Neural

Momoko Ishimaru1, Yoshifumi Okada2, Ryunosuke Uchiyama1

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Summary
This summary is machine-generated.

This study introduces a deep learning model using audio features for depression classification. While effective for recurring patients, it struggles to identify new cases, highlighting the need for localized speech analysis.

Keywords:
audio featureclassification modelcorrelationdepressiongraph convolutional neural network

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Area of Science:

  • Computational linguistics
  • Artificial intelligence in healthcare
  • Speech signal processing

Background:

  • Automatic depression classification relies on analyzing audio features reflecting vocal organ movements.
  • Understanding the interrelationships among audio features is crucial for accurate speech-based depression detection.

Purpose of the Study:

  • To develop a deep learning model for discriminating depression and its severity by analyzing correlations among audio features.
  • To evaluate the model's performance in classifying depression using graph structures and graph convolutional neural networks.

Main Methods:

  • Representing correlations among audio features as graph structures.
  • Utilizing a graph convolutional neural network (GCNN) to learn speech characteristics for classification.
  • Conducting experiments with both subject-overlapping (Setting 1) and subject-separated (Setting 2) training and testing data.

Main Results:

  • The GCNN model significantly outperformed state-of-the-art methods in Setting 1 (subjects included in both training and test data).
  • Classification accuracy in Setting 2 (completely separated subjects) was substantially lower than in Setting 1.
  • The model demonstrates effectiveness for recurring depression cases but faces challenges in identifying new instances.

Conclusions:

  • The proposed graph-based deep learning model is effective for discriminating recurring depression and its severity.
  • Detecting new depression cases requires further research, potentially focusing on localized, depression-specific speech regions.
  • Future practical applications may involve identifying and labeling specific vocal biomarkers within speech segments.