Detecting Sentences Types in the Standard Arabic Language

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.