As a continuation of our efforts towards tackling the problem of spoken Dialect Identification (DID) for Arabic languages, we present the QCRI-MIT Advanced Dialect Identification System (QMDIS). QMDIS is an automatic spoken DID system for Dialectal Arabic (DA). In this paper, we report a comprehensive study of the three main components used in the spoken DID task: phonotactic, lexical and acous...

Current Arabic lexicons, whether computational or otherwise, make no distinction between entries from Modern Standard Arabic (MSA) and Classical Arabic (CA), and tend to include obsolete words that are not attested in current usage. We address this problem by building a large-scale, corpus-based lexical database that is representative of MSA. We use an MSA corpus of 1,089,111,204 words, a pre-a...

The aim of this paper is to present an Arabic speech database that represents Arabic native speakers from all the cities of Saudi Arabia. The database is called the Saudi Accented Arabic Voice Bank (SAAVB). Preparing the prompt sheets, selecting the right speakers and transcribing their speech are some of the challenges that faced the project team. The procedures that met these challenges are h...

The Arabic language is a collection of multiple variants, among which Modern Standard Arabic (MSA) has a special status as the formal written standard language of the media, culture and education across the Arab world. The other variants are informal spoken dialects that are the media of communication for daily life. Arabic dialects differ substantially from MSA and each other in terms of phono...

The development of Language Engineering and Information Retrieval applications for Arabic require availability of sizeable, reliable corpora of modern Arabic text. These are not routinely available. This paper describes how we constructed an 18.5 million word corpus from Al-Hayat newspaper text, with articles tagged as belonging to one of 7 domains. We outline the profile of the data and how we...

Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We int...

In the Arab world, while Modern Standard Arabic is commonly used in formal written context, on sites like Youtube, people are increasingly using Dialectal Arabic, the language for everyday use to comment on a video and interact with the community. These user-contributed comments along with the video and user attributes, offer a rich source of multi-dialectal Arabic sentences and expressions fro...

The report addresses the basic problems of the Arabic language formalization based on analysis of linguistic errors in software products. Reviewing the principles of modern information systems operation the authors come to the conclusion that the existing methods of the Arabic formalization allow to note a shift towards the technological aspects of the linguistic processing of facts, however, t...

Arabic is not just one language, but rather a collection of dialects in addition to Modern Standard Arabic (MSA). While MSA is used in formal situations, dialects are the language of every day life. Until recently, there was very little dialectal Arabic in written form. With the advent of social-media, however, the landscape has changed. We provide the first romanized code-switched Algerian Ara...

Amajor problem with dialectal Arabic acoustic modeling is due to the very sparse available speech resources. In this paper, we have chosen Egyptian Colloquial Arabic (ECA) as a typical dialect. In order to benefit from existing Modern Standard Arabic (MSA) resources, a cross-lingual acoustic modeling approach is proposed that is based on supervised model adaptation. MSA acoustic models were ada...