This study explores the use of multimodal data fusion for depression detection, focusing on combining text and audio to improve the accuracy of identifying depressive states. It presents two multimodal networks—early fusion and late fusion—utilizing convolutional neural network (CNN) layers, bidirectional LSTM (Bi-LSTM) for sequence processing, and a self-attention mechanism to enhance the model’s ability to focus on critical parts of the data. By integrating audio (tone, pitch, rhythm) and text (word usage, sentence structure), the models are able to capture a fuller picture of the emotional and mental state of individuals, increasing the accuracy of detection. The study highlights the effectiveness of early fusion models, achieving an F1-score of 0.79 and an overall accuracy of 0.86 on the test dataset. These models show promise in diagnosing depression, particularly in cases where individuals may struggle to express their symptoms.
Takeaways:
- Multimodal Fusion for Improved Detection: The integration of both audio and text data significantly improves depression detection accuracy, capturing a fuller range of emotional and cognitive cues.
- Early vs. Late Fusion Models: The early fusion model outperforms the late fusion model in depression detection, indicating that combining data at an earlier stage enhances model performance.
- Robust Performance: The models achieved an F1-score of 0.79 and an accuracy of 0.86, suggesting the potential for high reliability in clinical settings.
- Attention Mechanisms: The use of self-attention mechanisms helps focus on key parts of the data, improving the model’s ability to detect depressive symptoms.
- Addressing Class Imbalance: The study recognizes the challenges posed by class imbalance in depression datasets and emphasizes the importance of effective preprocessing techniques to mitigate this issue.
