Speech Emotion Recognition System Using GMM Model
A speech emotion recognition system based on the Gaussian Mixture Model (GMM) framework, where GMM serves as a mathematical model for fitting data distributions. Discrepancies between observed data patterns and model outputs are expected since EM algorithm estimation of GMM parameters typically assumes incomplete data - meaning the algorithm computationally "completes" hidden or missing data components during parameter optimization. The system implementation involves feature extraction from speech signals, GMM parameter initialization, iterative EM updates for mean vectors, covariance matrices, and mixture weights, followed by maximum likelihood classification for emotion categorization.