Implementation of Vector Quantization-Based Speaker Recognition System in MATLAB Environment

This speaker recognition system utilizes vector quantization technology in MATLAB environment to perform real-time identification of speech samples ranging from 1 to 7 seconds from different individuals. The system achieves text-independent automatic speaker verification and provides the following implementation instructions: 1. Launch MATLAB and set the Current Directory to the folder containing VQ files (for example: E:vq). 2. In the Command Window, enter train(1s, 7) - this command performs feature extraction from 1-second WAV files and generates VQ codebooks. The system uses MFCC (Mel-Frequency Cepstral Coefficients) feature extraction algorithm to convert speech signals into characteristic vectors. The workspace will contain an ans file which should be saved as a .m file (e.g., 7.m) and stored in the 1s folder (where "1s" indicates 1-second speech duration). 3. In the Command Window, input test(4s/, 7, ans), where ans is the codebook file generated in step 1 (ensure 7.m is loaded in the workspace). The recognition algorithm compares input speech features with stored codebooks using distance measurement techniques like Euclidean distance or cosine similarity. The system will display the following matching results: - Speaker 1 matches with speaker 2 - Speaker 2 matches with speaker 3 - Speaker 3 matches with speaker 4 - Speaker 4 matches with speaker 6 - Speaker 5 matches with speaker 2 - Speaker 6 matches with speaker 7 - Speaker 7 matches with speaker 7 Note: 1s and 4s represent recordings of 7 different speakers, with their sequence randomly rearranged for testing purposes. The vector quantization implementation involves codebook generation using clustering algorithms like LBG (Linde-Buzo-Gray) and pattern matching through nearest neighbor search.