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@article{180708,
        author = {Mayank Salakke and Harshal Patil and Abhay Sawarkar and Devanshu Shyamsundar},
        title = {Real time Emotion Detection from Face and Voice using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2424-2436},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180708},
        abstract = {This paper presents an enhanced emotion detection and mental health monitoring system that extends our previous work on multimodal emotion recognition. The proposed system integrates facial expression analysis, voice processing, and text sentiment analysis to provide comprehensive emotional state assessment for early detection of potential mental health concerns. By incorporating temporal analysis of emotional pat- terns, the system can identify deviations that may indicate mental health issues such as depression, anxiety, or emotional distress. Our enhanced architecture achieves 96.2% accuracy on emotion classification and demonstrates promising results in early detection of mental health concerns, with 89.7% sensitivity in identifying emotional patterns associated with depression. Experimental evaluations showcase the system’s effectiveness in real-world scenarios with minimal computational overhead, making it suitable for continuous monitoring applications in healthcare, educational, and workplace environments. The paper also explores the ethical considerations and limitations of such systems, proposing guidelines for responsible implementation in mental health contexts.},
        keywords = {Emotion Detection, Mental Health, Deep Learn- ing, Multimodal Analysis, Facial Expression Recognition, Voice Analysis, Text Sentiment Analysis, Temporal Pattern Recognition, Depression Detection, Anxiety Detection},
        month = {June},
        }