Robust Multimodal Depression Detection via Targeted Fine-Tuning of RoBERTa and Wav2Vec2
The proposed paper presents a multimodal deeplearning approach to detect Depression from Clinical Interview data through transcript analysis and Acoustic Feature extraction. This was implemented by freezing both encoder models, RoBERTa (for Transcript Analysis) and Wav2Vec2 (for Acoustic Feature Extraction) except for...