A Multimodal Virtual Psychiatrist Interviewer and Mental Health Screener

The global increase in mental-health conditions such as anxiety, depression, and stress highlights the need for accessible and timely psychological evaluation. Conventional evaluation remains limited due to clinician shortages and the stigma associated with seeking help. This work presents a multimodal Virtual Psychiatrist Interviewer designed to facilitate adaptive and scalable early-stage mental-health screening. The proposed framework integrates DistilBERT for linguistic interpretation, a convolutional audio-emotion model to analyze vocal cues, and V2Face-based facial-affect recognition for visual understanding. An attention-driven fusion mechanism combines text, acoustic, and facial embeddings to capture complementary behavioral signals and produce robust preliminary assessments. The system is trained and evaluated on a curated mental health text dataset, the RAVDESS emotional speech corpus, and publicly available facial expression datasets. Experimental results demonstrate competitive performance on anxiety, depression, and stress detection tasks, while ablation studies confirm the contribution of each modality. The findings indicate the potential of the proposed system for real-time AI-assisted mental-health support