Viterbi Algorithm and Its Application to Indonesian Speech Recognition

Abstract

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 the name HInit. The HMM model is introduced briefly based on the theory of Markov Chain. We schematically outline the Viterbi method implemented by HInit. The iterative formal definition of the method which directs computer implementation 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. The performance of the algorithm for phone recognition rate on small Indonesian vocabulary is 80%, while the result is near perfect 95% for word recognition.