The standard Arabic language, like many other languages, contains a prosodic feature, which is hidden in the
speech signal. The studies related to this field are still in the preliminary stages. This fact results in restraining the
performance of the communication tools. The prosodic study allows people having all the communication tools needed in their
native language. Therefore, we propose, in this paper, a prosodic study between the various types of sentences in the standard
Arabic language. The sentences are recognized according to three modalities as the following: declarative, interrogative and
exclamatory sentences. The results of this study will be used to synthesize the different types of pronunciation that can be
exploited in several domains namely the man-machine communication. To this end, we developed a specific dataset, consisting
of the three types of sentences. Then, we tested two sets of features: prosodic features (Fundamental Frequency, Energy and
Duration) and spectrum features (Mel-Frequency Cepstral Coefficients and Linear Predictive Coding) as well their
combination. We adopted the Multi-Class Support Vector Machine (MC-SVM) as classifier. The experimental results are very
encouraging