MATLAB Code Implementation for SVM Classifier

This article introduces an SVM classifier specifically applied for speech signal classification and recognition. While the initial description is concise, we can further explore its extensive functionalities and application domains. By selectively implementing appropriate methods, developers can leverage the SVM classifier's advantages to solve various speech signal processing challenges. The implementation typically involves feature extraction from audio signals (such as MFCC coefficients), data normalization, and utilizing MATLAB's built-in functions like fitcsvm for model training with kernel function selection (linear, RBF, or polynomial). Key steps include parameter optimization through cross-validation and evaluating performance metrics like accuracy and confusion matrices. Further research should focus on optimizing feature engineering and hyperparameter tuning to unlock the classifier's full potential in speech processing applications.