Speech Emotion Recognition Simulation

This MATLAB-based speech emotion recognition simulation system implements user registration and detects emotional states such as happiness, anger, sadness, and joy during speech. The system achieves high accuracy with small user groups by leveraging machine learning algorithms and speech signal processing technologies. Through analysis of vocal characteristics and linguistic content in audio signals, the system infers the speaker's emotional state. During user registration, the system collects voice samples and uses them for training models to improve recognition accuracy in subsequent emotion detection processes. The implementation typically involves feature extraction using MFCC (Mel-Frequency Cepstral Coefficients) and prosodic features, followed by classification using algorithms like SVM (Support Vector Machines) or neural networks. By recognizing emotions in speech, this system helps users better understand emotional expressions during communication and provides more accurate feedback. While high accuracy is achievable with limited users, challenges emerge with larger groups due to variations in vocal characteristics and emotional expressions across individuals. The MATLAB code would typically include functions for audio preprocessing, feature extraction, model training, and real-time emotion classification. Practical applications require further optimization of algorithms and models to enhance system robustness and performance, potentially incorporating deep learning approaches like CNNs or RNNs for improved handling of diverse user populations and emotional variations.