Authors: Assistant Professor S.Venkateswara Rao, K. Vaishnavi
Abstract: Human speech conveys not only linguistic information but also valuable emotional cues that reflect an individual's psychological and behavioral state. Accurately identifying these emotions is becoming increasingly important for developing intelligent human–computer interaction systems capable of delivering natural, adaptive, and personalized communication experiences. Conventional speech emotion recognition methods often depend on handcrafted acoustic features and traditional classification techniques, which may struggle to capture the complex temporal and spectral characteristics of emotional speech. Recent advances in machine learning and deep learning have significantly improved the capability of automated systems to analyze speech signals and recognize emotional patterns with greater accuracy, enabling their application in domains such as virtual assistants, customer support, healthcare, education, and mental health monitoring. The effectiveness demonstrates that the CNN model achieves the highest recognition performance among the evaluated approaches, outperforming both SVM and LSTM in emotion classification accuracy while effectively learning complex acoustic representations from speech data. The integration of advanced preprocessing, discriminative feature extraction, and deep learning-based classification contributes to reliable and efficient emotion recognition across multiple emotional categories. The proposed framework provides a scalable solution for intelligent speech analysis and offers significant potential for enhancing emotion-aware applications in modern human–computer interaction systems by enabling machines to interpret and respond to human emotions more naturally and effectively.
