Feature Extraction, Model Building, and Recognition Decision

In MATLAB, we can utilize the following source code to accomplish three critical stages: speech signal input, feature extraction, and model-based recognition decision. First, the code enables speech signal acquisition through functions like `audioread()` or real-time input interfaces, facilitating subsequent processing and analysis. Second, it incorporates robust feature extraction methods such as Mel-Frequency Cepstral Coefficients (MFCC) extraction using `mfcc()` or spectral feature computation, which transform raw speech signals into discriminative feature vectors for model training. Finally, the implementation includes algorithms for model building (e.g., Gaussian Mixture Models via `gmdistribution` or Hidden Markov Models with dedicated toolkits) and recognition decision logic, employing classification functions like `classify()` or dynamic time warping for accurate and reliable speech recognition tasks. In summary, this code provides a complete toolkit for speech signal processing and recognition, integrating signal handling, feature engineering, and machine learning components to demonstrate practical applications of speech technology.