MATLAB-Based Speech Recognition Program with MFCC and SVM Implementation

This example program implements a speech recognition system using MATLAB, employing traditional MFCC (Mel-Frequency Cepstral Coefficients) feature extraction and utilizing the widely-adopted SVM (Support Vector Machine) classifier, making it suitable for comparative experiments. The MFCC extraction process typically involves pre-emphasis, framing, windowing, FFT, Mel-filterbank application, and DCT transformation to generate robust acoustic features. The SVM classifier implementation uses kernel functions to create optimal decision boundaries for speech pattern classification. To further improve recognition accuracy, consider integrating more advanced feature extraction algorithms such as deep learning models (e.g., using spectrograms with CNN architectures). Additionally, exploring alternative classifiers like Random Forests (using MATLAB's TreeBagger function) and Neural Networks (via Deep Learning Toolbox) would enable comprehensive performance comparisons between different algorithms. Implementation-wise, increasing training datasets and optimizing model parameters through techniques like grid search cross-validation could enhance model generalization and robustness. In summary, through continuous exploration and improvement incorporating advanced MATLAB toolboxes and optimization techniques, this speech recognition program can be further refined to achieve higher accuracy and reliability in practical applications.