MATLAB Speech Recognition Code Using Hidden Markov Model (HMM) Algorithm

This documentation presents MATLAB speech recognition code employing the Hidden Markov Model (HMM) algorithm. The implementation transforms audio signals into digital representations through feature extraction techniques where acoustic characteristics are converted into numerical feature vectors. The code incorporates mathematical models including Gaussian Mixture Models (GMM) for probability density estimation of acoustic features and Dynamic Time Warping (DTW) for temporal alignment of speech patterns. In the implementation, the HMM algorithm processes these feature vectors through state transitions and observation probabilities to decode spoken words. The code typically involves training HMM parameters using the Baum-Welch algorithm and performing recognition through the Viterbi algorithm for optimal path decoding. Preprocessing techniques such as speech enhancement and noise reduction are integrated to improve model robustness and accuracy. The feature extraction phase may include Mel-Frequency Cepstral Coefficients (MFCC) computation using functions like mfcc() from MATLAB's Audio Toolbox, while HMM training utilizes forward-backward procedures implemented through custom matrix operations. The integration of these components - from signal preprocessing and feature extraction to HMM modeling and pattern matching - creates a comprehensive framework for building accurate and efficient speech recognition systems. The MATLAB implementation provides modular functions for each processing stage, allowing for systematic optimization of recognition performance through parameter tuning and algorithm refinement.