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Related Concept Videos

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Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
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SBT-Net: a tri-cue guided multimodal fusion framework for depression recognition.

Yujie Huo1, Weng Howe Chan2,3, Ahmad Najmi Bin Amerhaider Nuar1

  • 1Faculty of Computing, Universiti Teknologi Malaysia, UTM Skudai, Johor Bahru, Johor, 81310, Malaysia.

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Summary

This study introduces SBT-Net, a new framework for detecting depression using audio and text. It achieves high accuracy by integrating semantic guidance, bias-aware fusion, and emotional trend modeling for robust multimodal analysis.

Keywords:
Bias-guided tensor product attentionEmotional trend modelingModal gating mechanismMultimodal depression recognition

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Area of Science:

  • Computational psychiatry
  • Artificial intelligence in healthcare
  • Multimodal machine learning

Background:

  • Early depression detection is crucial for public health.
  • Current multimodal methods face challenges like incomplete data, semantic inconsistencies, and fluctuating emotional states.
  • Robust depression detection requires advanced analytical frameworks.

Purpose of the Study:

  • To propose SBT-Net, a novel Semantic-Bias-Trend guided framework for robust depression detection using audio and text data.
  • To address limitations of existing multimodal depression detection methods.
  • To improve the accuracy and reliability of automated depression assessment.

Main Methods:

  • Developed SBT-Net, incorporating a semantically guided cross-modal gating (SGCMG) mechanism for feature filtering.
  • Integrated a bias-guided tensor product attention (BG-TPA) mechanism for enhanced inter-modal fusion and alignment.
  • Utilized an emotion trend modeling (ETM) module to capture temporal dynamics of depressive states.

Main Results:

  • SBT-Net achieved 93.0% accuracy, 0.93 F1 score, and 0.92 recall on benchmark datasets (DAIC-WOZ, EATD-Corpus).
  • Performance surpassed competitive baseline models across multiple evaluation metrics.
  • Ablation studies confirmed the significant contributions of individual and combined modules.

Conclusions:

  • The proposed SBT-Net framework demonstrates superior performance in multimodal depression detection.
  • Integrating semantic guidance, bias-aware fusion, and emotional trend modeling enhances robustness.
  • Findings suggest a promising direction for advancing automated mental health monitoring solutions.