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Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network With Domain-Adversarial
Summary
This study introduces a novel patient-independent framework for automatic depression detection using multimodal deep learning. The approach enhances generalization and achieves state-of-the-art performance by integrating acoustic and textual data with domain generalization techniques.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computational Linguistics
Background:
- Deep learning models for depression detection often struggle with generalization due to variations between speakers.
- Domain shift, caused by interspeaker variability, limits the real-world applicability of current automatic depression detection systems.
Purpose of the Study:
- To develop the first patient-independent multimodal depression detection framework that improves generalization.
- To address the challenge of domain shift in automatic depression detection by incorporating domain generalization (DG).
Main Methods:
- A multimodal framework integrating acoustic and textual data using bidirectional long short-term memory (BiLSTM) with attention mechanisms.
- Segment-level fusion and domain generalization (DG) techniques, including a gradient reversal layer inspired by domain-adversarial training of neural networks (DANNs), were employed to promote domain-invariant representations.
- Experiments were conducted on the Androids-Corpus dataset using a fivefold cross-validation protocol, evaluating MelSpec and ItalianBERT as optimal feature extractors at a 30-s segment duration.
Main Results:
- The proposed framework achieved 93.2% accuracy, 93.2% precision, 96.2% recall, and 94.2% F1-score, surpassing existing benchmarks.
- The integration of DG led to a 2.5% increase in accuracy and a 3.3% increase in F1-score compared to the baseline.
- Ablation studies confirmed the significant contributions of multimodal fusion, deep architecture choices, and DG to the model's robustness and generalizability.
Conclusions:
- The developed patient-independent multimodal depression detection framework demonstrates superior performance and generalization capabilities.
- The study highlights the effectiveness of combining multimodal data, advanced deep learning architectures, and domain generalization techniques for robust depression detection.
- This work offers a promising approach for developing more reliable and widely applicable automatic depression detection systems.