Robust Voice Activity Detection and Emotion Recognition using Machine Learning and LLM-based Framework

This research presents a robust voice-activated detection system for real-time speech emotion recognition by integrating traditional acoustic feature extraction with transformer-based embeddings. The system effectively captures both spectral and contextual information from speech signals and classifies emotions with satisfactory accuracy and low latency. Experimental results demonstrate improved performance compared to conventional methods, especially in real-time scenarios. The proposed approach offers a scalable and practical solution for applications in healthcare, customer service, education, and intelligent human–computer interaction. With further enhancements such as deep learning integration, noise robustness, and multimodal analysis, the system has strong potential to evolve into a highly efficient and adaptive emotion-aware framework for real-world deployment.

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