BiLSTM with Domain-Adversarial Training for Depression Detection
A recent study available on arXiv introduces a framework for multimodal domain generalization aimed at automating depression detection. This research tackles the challenge of limited generalization caused by variations among different speakers. The proposed framework combines bidirectional Long Short-Term Memory (BiLSTM) with attention mechanisms that operate both within and across modalities, as well as segment-level fusion, utilizing acoustic and textual data. To further enhance generalization, it incorporates a gradient reversal layer from Domain-Adversarial Training of Neural Networks (DANN), which helps create domain-invariant representations by adversarially restricting the model's capacity to recognize individual speakers, thereby minimizing patient-specific bias. Validation on the Androids-Corpus dataset through 5-fold cross-validation confirms the method's effectiveness.
Key facts
- First patient-independent multimodal depression detection framework incorporating domain generalization
- Uses BiLSTM with intra- and cross-modal attention mechanisms
- Employs segment-level fusion for decision-making
- Applies gradient reversal layer from DANN for domain-invariant representations
- Experiments conducted on Androids-Corpus dataset
- Uses 5-fold cross-validation
- Addresses domain shift from inter-speaker variability
- Jointly leverages acoustic and textual modalities
Entities
Institutions
- arXiv