Speaker Recognition using Vector Quantization (VQ) Method with MATLAB Implementation

This MATLAB code demonstrates speaker recognition using the Vector Quantization (VQ) method, specifically designed for beginners who are new to vector quantization principles. Vector quantization is a signal processing technique that discretizes continuous signals by converting continuous speech signals into a series of discrete vectors. The implementation uses a codebook-based approach where the system creates representative vectors (codebook) from training data and classifies unknown speakers by comparing their feature vectors against stored codebooks. Key algorithm components include: - Feature extraction using Mel-Frequency Cepstral Coefficients (MFCC) for speech parameterization - LBG (Linde-Buzo-Gray) algorithm for codebook generation through iterative clustering - Distance measurement (typically Euclidean distance) for vector similarity comparison - Classification decision based on minimum distortion criteria The code structure includes functions for: 1. Training phase: Creating speaker-specific codebooks from enrolled speech samples 2. Testing phase: Matching input speech features against stored codebooks 3. Evaluation module: Calculating recognition accuracy and performance metrics Through this implementation, beginners can learn how to apply VQ methodology for speaker identification while gaining practical experience with MATLAB's signal processing and pattern recognition capabilities. The code provides hands-on understanding of feature vector quantization, pattern matching algorithms, and speaker modeling techniques in a practical MATLAB environment.