Viterbi Extraction tutorial with Hidden Markov Toolkit
This is an incremental tutorial for practitioners using HTK to understand the Viterbi extraction method.
This paper revisits the Viterbi extraction algorithm for Hidden Markov Model (HMM) parameters as implemented in the Hidden Markov Toolkit (HTK), specifically the HInit program, by outlining its variations, iterative definition, and manual calculations with graphical illustrations. It focuses on describing the algorithm's implementation details using independent Gaussian distributions, without evaluating its performance or accuracy in specific applications.
An algorithm used to extract HMM parameters is revisited. Most parts of the extraction process are taken from implemented Hidden Markov Toolkit (HTK) program under name HInit. The algorithm itself shows a few variations compared to another domain of implementations. The HMM model is introduced briefly based on the theory of Discrete Time Markov Chain. We schematically outline the Viterbi method implemented in HTK. Iterative definition of the method which is ready to be implemented in computer programs is reviewed. We also illustrate the method calculation precisely using manual calculation and extensive graphical illustration. The distribution of observation probability used is simply independent Gaussians r.v.s. The purpose of the content is not to justify the performance or accuracy of the method applied in a specific area. This writing merely to describe how the algorithm is performed. The whole content should enlighten the audience the insight of the Viterbi Extraction method used by HTK.