Speech Emotion Recognition System Using GMM Model

In speech emotion recognition systems, the Gaussian Mixture Model (GMM) is widely employed for its probabilistic modeling capabilities. Although GMM fundamentally serves as a mathematical framework for approximating data distributions, the Expectation-Maximization (EM) algorithm used for parameter estimation operates under the assumption of incomplete observed data. This implies that the visible data distribution doesn't represent the true underlying distribution, as the algorithm computationally "completes" latent data components during the E-step (expectation) where posterior probabilities are calculated, and the M-step (maximization) where parameters are updated. Consequently, additional data analysis is essential to better comprehend GMM's applications and limitations in emotional speech processing. Practical implementations typically involve: 1) Extracting spectral features (MFCCs) from audio segments, 2) Initializing GMM parameters using k-means clustering, 3) Running EM iterations until log-likelihood convergence, and 4) Handling outliers through robust covariance estimation or mixture component pruning. These approaches enhance the system's accuracy and robustness when processing diverse emotional datasets with varying speaker characteristics and acoustic environments.