Unaligned Multimodal Sequences for Depression Assessment From Speech.

Area of Science:

• Mental Health Research
• Machine Learning Applications
• Biomedical Signal Processing

Background:

• Automatic depression severity assessment is crucial for mental healthcare.
• Challenges include unaligned multimodal data and limited annotated datasets for machine learning.
• Objective markers from text, audio, and video offer potential for depression analysis.

Purpose of the Study:

• To propose a novel cross-modal framework for automatic depression severity assessment.
• To address challenges of unaligned multimodal sequences and data scarcity in machine learning.
• To improve the accuracy of depression severity estimation using multimodal data.

Main Methods:

• Extracting low-level descriptions (LLDs) from text, audio, and video modalities.
• Utilizing a cross-modal attention mechanism for multimodal fusion and enhanced feature representation.
• Employing Self-Attention Generative Adversarial Networks (SAGAN) to augment training data.
• Mapping fused features to Patient Health Questionnaire (PHQ-8) scores.

Main Results:

• The proposed cross-modal framework demonstrated effectiveness in depression severity assessment.
• Multimodal fusion via cross-modal attention improved feature learning accuracy.
• SAGAN successfully increased available training data for depression analysis.
• Experiments were validated on the AVEC 2017 and AVEC 2019 datasets.

Conclusions:

• The novel cross-modal framework offers a promising approach for objective depression severity assessment.
• Integrating multimodal data with advanced machine learning techniques can overcome existing research challenges.
• This method has the potential to enhance automated mental health monitoring and diagnosis.