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@article{178909,
        author = {Veerendar S and Dr. Karthick Raghunath and Vasanth Raj and Robel B},
        title = {AI-Enhanced Telemedicine: Real-Time Speech Emotion Recognition and Contextual Recommendations Using LSTM and Generative AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4902-4909},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178909},
        abstract = {This paper presents a context-aware speech emotion recognition system tailored for telemedicine, leveraging multi-input deep learning models and real-time voice analysis to detect patient emotions. The proposed system integrates audio feature extraction using Mel-frequency cepstral coefficients (MFCCs), LSTM-based emotion classification, and personalized recommendation generation via the Gemini Pro API. A Streamlit-based interface facilitates seamless interaction, while real-time audio input and session tracking enable clinicians to monitor patient emotional trends. Experimental results demonstrate the model’s effectiveness in identifying seven distinct emotions, offering a novel approach to enhancing empathetic care in remote medical consultations.},
        keywords = {Speech Emotion Recognition, Telemedicine, Deep Learning, Context-Aware Systems, MFCC, LSTM, Streamlit, Gemini Pro API, Real-Time Emotion Detection, Personalized Recommendations},
        month = {May},
        }