Abstract: In the era of natural computing, automatic recognition of effective natural communication database for recognition is having major challenges. Human computer interface is always proved to be stimulating area in the speech and the language recognition domain. Speech is the important choice of man-machine communication. The objective of the speech recognition is to cultivate statistical methods and its use in real time automatic speech recognition systems. The statistical model of speech recognition plays an important role in the development of real time system. Arabic is a global language for Iraq country and has universal acceptance. This is an official language at many institutes in Iraq. This study addresses the implementation of an Advance Speech Recognition system (ASR) for an Arabic language towards identification of natural communication aspects. The statistical modeling and implementation of the system is done in this study. This research contributed the development, Arabic speech database for isolated (character, digit, word) and connected sentences which are used day to day activity in Arabic community. In this study, 10 subject databases were developed using the PRAAT Software in an office environment. The size of collecting database is consisting of 1200 samples of isolated words and 800 samples of connected sentences, the size of the database is 2000. The experimental analysis of Arabic natural communication is tested using the Mel Frequency Cepstral Coefficient (MFCC) feature extraction technique and dynamic time warping algorithm. The isolated natural recognition system gives 94% accuracy and continuous spoken natural communication proves the 87%. The Natural isolated recognition system gives a robust and dynamic performance than continuous speech recognition. This natural communication based emotional spoken database will be used for the emotive aware cloud computing and spoken interface domain.