A MATLAB Implementation of HMM for Isolated Word Speech Recognition

This is a MATLAB implementation of Hidden Markov Models (HMMs) designed specifically for isolated word speech recognition. HMMs are statistical models that describe hidden Markov processes, widely applied in speech recognition to identify different isolated words through learning and inference mechanisms. The MATLAB implementation provides a comprehensive framework that includes key functions for model parameter training using the Baum-Welch algorithm and recognition tasks utilizing the Viterbi algorithm. The code structure features modular design with separate components for feature extraction (typically MFCCs), model initialization, training iteration, and probability calculation. This method offers researchers and developers a practical tool that simplifies the implementation of HMM-based speech recognition systems, enabling efficient parameter optimization and recognition accuracy improvement through customizable training cycles and emission probability configurations. The implementation supports flexible model customization including state numbers and Gaussian mixture components, making it suitable for various isolated word recognition scenarios.