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A lightweight approach based on cross-modality for depression detection.

Eunchae Lim1, Min Jhon2, Ju-Wan Kim3

  • 1Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea.

Computers in Biology and Medicine
|January 7, 2025
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Summary
This summary is machine-generated.

This study introduces a lightweight multimodal model using audio and text for early depression detection. The model shows promise for cross-language application, aiding depression diagnosis in daily life.

Keywords:
Cross-modalityDepression datasetDepression detectionMultimodal fusion

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Area of Science:

  • Computational psychiatry
  • Machine learning in healthcare
  • Speech and language processing

Background:

  • Early depression detection is critical to prevent suicide, but traditional methods like interviews and questionnaires face diagnostic challenges due to depression's heterogeneity.
  • Limitations in current diagnostic approaches necessitate exploring biological markers and easily collectable data like audio for improved detection.
  • Investigating biological aspects of depression is essential to overcome the shortcomings of subjective and time-consuming traditional diagnostic methods.

Purpose of the Study:

  • To propose a novel multimodal fusion cross-modality model for detecting depression using audio and text data.
  • To develop a lightweight model with reduced parameters for deployment on pervasive devices while maintaining accuracy.
  • To evaluate the model's cross-language applicability and its ability to learn universal depression characteristics.

Main Methods:

  • Developed a multimodal fusion model integrating audio and textual features for depression detection.
  • Designed the model to be lightweight, optimizing parameter count for efficiency on edge devices.
  • Evaluated the model on diverse datasets (DAIC, EmoDB, Korean Depression) in English, Chinese, and Korean.

Main Results:

  • Achieved significant F1-scores: 0.67 (DAIC), 0.81 (EmoDB), and 0.61 (Korean Depression).
  • Demonstrated successful cross-language performance, indicating the model learns language-independent depression indicators.
  • The lightweight design maintained accuracy with fewer parameters, suitable for real-world applications.

Conclusions:

  • The proposed multimodal model effectively detects depression using audio and text, offering a promising avenue for early and accessible diagnosis.
  • The model's lightweight and cross-language capabilities suggest potential for widespread deployment on various devices for global mental health monitoring.
  • Combining nonlinguistic speech and linguistic text features allows the model to capture distinctive depression characteristics across different languages.