MATLAB-Based Speaker Recognition System Implementing Gaussian Mixture Models (GMM)

The MATLAB-based speaker recognition system utilizing Gaussian Mixture Models (GMM) is a sophisticated implementation for identifying speakers through voice characteristics. This system employs MATLAB programming to model and match acoustic features, achieving accurate speaker identification. The implementation follows a structured pipeline involving data acquisition, feature extraction, model training, and recognition phases. Key algorithmic components include MFCC (Mel-Frequency Cepstral Coefficients) feature extraction using MATLAB's audio processing toolbox, GMM training through Expectation-Maximization algorithm, and likelihood-based classification for speaker matching. By implementing GMM-based speaker recognition, developers can effectively analyze speech signal patterns and achieve higher recognition accuracy, with MATLAB functions like `gmdistribution.fit` facilitating model training and `pdf` methods enabling probability calculations during verification. The system demonstrates practical application of statistical modeling techniques for biometric authentication systems.