The Arabic language is a collection of dialectal variants along with the standard form, Modern Standard Arabic (MSA). MSA is used in official Settings while the dialectal variants (DA) correspond to the native tongue of the Arabic speakers. Arabic speakers typically code switch between DA and MSA, which is reflected extensively in written online social media. Automatic processing such Arabic ge...

The computational handling of Modern Standard Arabic is a challenge in the field of natural language processing due to its highly rich morphology. However, several authors have pointed out that the Arabic morphological system is in fact extremely regular. The existing Arabic morphological analyzers have exploited this regularity to variable extent, yet we believe there is still some scope for i...

The signal sound contains many different features, including Voice Onset Time (VOT), which is a very important feature of stop sounds in many languages. The only application of VOT values is stopping phoneme subsets. This subset of consonant sounds is stop phonemes exist in the Arabic language, and in fact, all languages. Very important subsets of Semitic language’s consonants are the Emphatic ...

Social networks and users’ interactions are distinct features for the current Web. They constitute a fundamental part of Web 2.0, where people produce, disseminate, and consume information in new interactive forms where users are not only passive information receivers. Social media succeed to attract a large portion of online users, which explains the explosive growth of social media in terms o...

Modern standard Arabic (MSA) is the official language of spoken and written Arabic media. Colloquial Arabic (CA) is the set of spoken variants of modern Arabic that exist in the form of regional dialects. CA is used in informal and everyday conversations while MSA is formal communication. An Arabic speaker switches between the two variants according to the situation. Developing an automatic spe...

The written form of the Arabic language, Modern Standard Arabic (MSA), differs in a nontrivial manner from the various spoken regional dialects of Arabic – the true “native languages” of Arabic speakers. Those dialects, in turn, differ quite a bit from each other. However, due to MSA’s prevalence in written form, almost all Arabic datasets have predominantly MSA content. In this article, we des...

Automatic speech segmentation is an essential tool for building large corpora for training continuous speech recognition systems. Manual segmentation of speech is both time consuming and an error-prone task. Several automatic segmentation systems have been proposed based on the acoustical features of the speech 5] 11]. In this paper, we present a novel technique for automatic seg-mentation of A...

There are millions of people in the world speak many languages. To communicate with each other it is necessary to know the language which we use. To do this operation we use language identification system. In general, Automatic Speech Recognition for English and other languages has been the subject of most researches in the last forty years. Arabic language research has been growing very slowly...

This paper presents a novel Arabic morphological generator (AMG) for Modern Standard Arabic (MSA) which is designed and implemented using Prolog. The AMG is used to generate inflected forms of words used for the indexing of Arabic audio. These words are also the relevant terms in the Arab authority system (library information retrieval system) used in this study. The AMG generates inflected Ara...

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their comb...