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 phonology, morphology, lexical choice and syntax. In this paper, we describe a system that automatically identifies the Arabic dialect (Gulf, Iraqi, Levantine, Egyptian and MSA) of a speaker given a sample of his/her speech. The phonotactic approach we use proves to be effective in identifying these dialects with considerable overall accuracy -81.60% using 30s test utterances.
For spoken dialect identification, REF described a system that can identify the Arabic dialect from a spoken text using acoustic features.
Spoken Arabic Dialect Identification Using Phonotactic Modeling
Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages
Computer Science