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Related Experiment Video

Updated: Jun 24, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Depression recognition using voice-based pre-training model.

Xiangsheng Huang¹, Fang Wang¹, Yuan Gao¹

¹School of Biomedical Engineering, South-Central Minzu University, No.182, Minzu Avenue, Hongshan District, Wuhan City, 430074, Hubei Province, China.

Scientific Reports

|June 3, 2024

Summary

This study introduces an AI method using wav2vec 2.0 for depression detection from voice data. The approach achieves high accuracy in classifying depression, aiding early screening.

Keywords:

DAIC-WOZ Depression Pre-training model Voice features Wav2vec 2.0

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Last Updated: Jun 24, 2025

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Area of Science:

Artificial Intelligence
Computational Linguistics
Clinical Psychology

Background:

Early depression screening improves patient diagnosis and treatment outcomes.
Voice data shows potential for depression detection, but dataset size limitations persist.
Existing methods struggle with insufficient data for robust depression identification.

Purpose of the Study:

To develop and evaluate an artificial intelligence (AI) method for effective depression identification using voice data.
To address the challenge of limited dataset size in voice-based depression detection.
To leverage pre-trained models for enhanced feature extraction in depression recognition.

Main Methods:

Utilized the wav2vec 2.0 model as a feature extractor for raw audio data.
Employed a fine-tuning network for depression classification based on extracted voice features.
Trained and validated the model on the DAIC-WOZ dataset.

Main Results:

Achieved high accuracy in binary classification (0.9649) and multi-classification (0.9481) for depression.
Demonstrated excellent performance with low Root Mean Square Error (RMSE) values (0.1875 for binary, 0.3810 for multi-classification).
Showcased strong generalization ability of the wav2vec 2.0 model in depression recognition.

Conclusions:

The proposed AI method is effective for depression screening using voice analysis.
The wav2vec 2.0 model offers a practical and applicable solution for early depression detection.
This approach can serve as a valuable tool for clinicians in identifying depression.