Speaker Recognition and Training System with Comprehensive Source Code

The speaker recognition and training system includes comprehensive source code with detailed implementations. The code covers complete workflows from speech signal processing (including pre-emphasis, framing, and windowing) to feature extraction techniques (such as MFCC, LPCC, and PNCC), and advanced model training approaches (including GMM-UBM, i-vector, and deep learning architectures). These implementations demonstrate practical algorithms for voiceprint pattern analysis and classification. Users can leverage this codebase to enhance their understanding of speaker recognition systems and apply them in real-world scenarios. The modular design allows for customization and optimization based on specific application requirements, enabling improvements in system performance and recognition accuracy through parameter tuning and algorithm enhancements. We encourage researchers and developers to modify and extend these implementations for their specific use cases. May this resource contribute to your learning and advancement in voice biometric technologies!