MATLAB Hidden Markov Model (HMM) Toolbox

This article introduces the MATLAB Hidden Markov Model (HMM) Toolbox, a robust software package designed for implementing various machine learning tasks such as speech recognition and natural language processing. The toolbox provides comprehensive algorithms and utilities that simplify model training and evaluation workflows. For instance, it includes specialized functions for time-series data processing (e.g., hmmtrain for Baum-Welch parameter estimation and hmmdecode for state path inference) to facilitate efficient data analysis. Additionally, the toolbox integrates visualization tools like state transition diagrams and probability distribution plots (implemented via hmmplot) to enhance interpretability of models and datasets. Key features support forward-backward algorithms, Viterbi path calculations, and emission probability handling through modular function design. In summary, the MATLAB HMM Toolbox is an indispensable resource for researchers and engineers engaged in data-driven modeling and pattern recognition applications.