  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.
