Speaker Recognition System Implementation

This code implements a speaker recognition system in MATLAB using the Vector Quantization (VQ) algorithm. Vector Quantization is a digital signal processing technique that discretizes continuous signals by mapping them to a finite set of vectors. The algorithm works by converting voice signals into feature vectors and comparing them against a database of pre-stored speaker templates for identification. Key implementation aspects include: - Feature extraction from audio signals using techniques like MFCC (Mel-Frequency Cepstral Coefficients) - Codebook generation through clustering algorithms such as LBG (Linde-Buzo-Gray) - Distance calculation using measures like Euclidean distance for vector comparison - Database matching through nearest-neighbor classification This system can be applied in various voice recognition applications, including voice-controlled interfaces and security systems requiring speaker authentication. The codebase allows for further modifications and optimizations to improve recognition accuracy and system performance through techniques like: - Enhanced feature selection algorithms - Improved clustering methods - Real-time processing optimizations - Noise reduction preprocessing The modular structure enables easy integration with larger speech processing frameworks and provides a foundation for developing more advanced speaker recognition systems.