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Multimodal Depression Detection An Integrated Multimodal Framework for Automated Depression Severity Assessment

Author(s) Anuj Sahu, Chandra Prakash Singar, Puja Gupta, Divyansh Ganote, Sanjay Patil
Country India
Abstract Depression remains one of the most prevalent mental health disorders globally, yet early detection remains challenging due to its multifaceted and heterogeneous clinical presentation [1]. This study presents a novel multimodal depression detection system that integrates five distinct analytical modalities: facial expression analysis, vocal tone assessment, video-based emotion recognition, text sentiment analysis, and clinical questionnaire evaluation. Using a probability-weighted scoring mechanism combined with an unequal weighting scheme that prioritizes explicit user input, the system generates a comprehensive depression severity score on a 0–100 scale with clinically relevant classification thresholds. The architecture leverages convolutional neural networks (CNNs) [4] for image and video processing, long short-term memory (LSTM) networks
[5] for audio analysis, a trained sequence model for text classification, and large language model (LLM) integration via the Groq API for open-ended questionnaire assessment. By combining noisy ambient snapshots with explicit clinical input, the system achieves a holistic psychological profile that extends beyond single-modality approaches [8]. This paper describes the technical implementation, mathematical framework, and clinical classification methodology underlying this integrated system.
Keywords depression detection, multimodal analysis, machine learning, emotion recognition, clinical assessment, CNN, LSTM, NLP, LLM
Field Engineering
Published In Volume 7, Issue 5, May 2026
Published On 2026-05-14
DOI https://doi.org/10.70528/IJLRP.v7.i5.2191
Short DOI https://doi.org/hb379t
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