Automated depression analysis using convolutional neural networks from speech.

Area of Science:

• Affective computing
• Artificial intelligence
• Speech signal processing

Background:

• Automated depression diagnosis systems are gaining interest for clinical efficiency.
• Speech features contain valuable information for depression severity assessment.
• Manual feature engineering is labor-intensive and subjective.

Purpose of the Study:

• To propose a hybrid feature approach combining hand-crafted and deep-learned features for depression severity diagnosis from speech.
• To overcome limitations of manual feature selection in automated depression detection.
• To improve the accuracy and robustness of speech-based depression diagnosis.

Main Methods:

• Utilized Deep Convolutional Neural Networks (DCNN) to extract deep-learned features from spectrograms and raw speech.
• Extracted Median Robust Extended Local Binary Patterns (MRELBP) as hand-crafted features from spectrograms.
• Implemented joint fine-tuning layers to integrate DCNN and MRELBP features for enhanced depression recognition.
• Applied a data augmentation technique to address challenges with small sample sizes.

Main Results:

• The proposed hybrid feature method demonstrated robust and effective performance in diagnosing depression severity.
• Achieved superior depression recognition performance compared to state-of-the-art audio-based methods.
• Validated effectiveness on the AVEC2013 and AVEC2014 depression databases.

Conclusions:

• The combination of deep-learned and hand-crafted features offers a powerful approach for speech-based depression severity diagnosis.
• The proposed method provides a more efficient and objective alternative to traditional manual feature engineering.
• This research contributes to advancing automated systems for mental health assessment.