Arabic Language is one of the most widely-spoken languages in the world, Arabic Speech
Recognition is one of the topics that need more attention from the research community. In this paper we
use Hidden-Markov modeling (HMM) to develop an efficient phoneme recognition engine for Arabic.
An HMM has been trained on ELRA Database and we study the effect of different parameters included
in the decoding Algorithm known as Viterbi. The effect of increasing the number of Gaussian Mixtures
components is also studied in output density of HMM. Results of each case has been presented and
suggestions for enhancing performance have been also introduced.