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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Depression screening with textual and audio features based on large language models and machine learning.

Yu Jin1, Xin Chen1, Xintian Hong2

  • 1Department of Statistics, Faculty of Arts and Sciences, Beijing Normal University, Beijing, China.

Journal of Affective Disorders
|November 18, 2025
PubMed
Summary
This summary is machine-generated.

Integrating audio and textual data significantly improves depression screening accuracy. Multimodal machine learning models, particularly Random Forest Regression (RFR), show superior performance by analyzing speech patterns and language sentiment.

Keywords:
Depression screeningLarge language modelsMachine learningMultimodal fusion

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Area of Science:

  • Psychology
  • Computer Science
  • Data Science

Background:

  • Depression screening often relies solely on textual data, potentially missing crucial psychomotor and affective changes.
  • Audio features offer valuable insights into emotional and behavioral aspects of depression.

Purpose of the Study:

  • To integrate textual and audio features for enhanced depression screening.
  • To compare the efficacy of various machine learning models using multimodal data.

Main Methods:

  • Utilized a multimodal dataset of 1275 adolescents (12-16 years) including PHQ-9 scores, interview responses, and audio recordings.
  • Extracted textual features using Large Language Models (LLMs) for suicide risk, emotional polarity, and depression severity.
  • Analyzed audio data via mel-spectrograms, MFCCs, and chroma features using a fine-tuned U-Net model to assess emotional status.

Main Results:

  • Multimodal fusion outperformed unimodal (text-only, audio-only) approaches, achieving the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
  • The Random Forest Regression (RFR) model demonstrated the highest accuracy (0.98) and precision (0.98) for depression prediction.
  • Key predictive features included textual indicators of depression severity, suicide risk, and emotional polarity, alongside audio-derived emotional features (happy, angry, neutral, surprise).

Conclusions:

  • Combining audio and textual data significantly enhances depression screening accuracy.
  • Future research should explore incorporating facial expressions and physiological indicators for further improvements.