Speaker Recognition Implementation Using Gaussian Mixture Models

I have developed source code that implements speaker recognition using Gaussian Mixture Models (GMMs). This implementation demonstrates high effectiveness in accurately identifying speakers through a comprehensive approach involving feature extraction and model training. The core implementation employs Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, capturing essential vocal characteristics from audio signals. The GMM training process utilizes the Expectation-Maximization (EM) algorithm to optimize model parameters, ensuring precise representation of individual speaker patterns. When applied to real-world speech data, the system achieves exceptional recognition accuracy. Further optimizations include parameter tuning and robustness enhancements through techniques like covariance regularization and model adaptation. Overall, this source code serves as a valuable tool in speech recognition applications, providing a solid foundation for speaker identification systems with practical implementation details for feature vector processing, model initialization, and probability scoring mechanisms.