Hidden Markov models with context-sensitive observations for grapheme-to-phoneme conversion

Abstract-Arabic is the official language overall Arab countries, it is used for official speech, news-papers, public administration and school. In Parallel, for everyday communication, nonofficial talks, songs and movies, Arab people use their dialects which are inspired from Standard Arabic and differ from one Arabic country to another. These linguistic phenomenon is called disglossia, a situation in which two distinct varieties of a language are spoken within the same speech community. It is observed Throughout all Arab countries, standard Arabic widely written but not used in everyday conversation, dialect widely spoken in everyday life but almost never written. Thus, in NLP area, a lot of works have been dedicated for written Arabic. In contrast, Arabic dialects at a near time were not studied enough. Interest for them is recent. First work for these dialects began in the last decade for middle-east ones. Dialects of the Maghreb are just beginning to be studied. Compared to written Arabic, dialects are under-resourced languages which suffer from lack of NLP resources despite their large use. We deal in this paper with Arabic Algerian dialect a non-resourced language for which no known resource is available to date. We present a first linguistic study introducing its most important features and we describe the resources that we created from scratch for this dialect.

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Hidden Markov models with context-sensitive observations for grapheme-to-phoneme conversion

Cited by 8 publications

References 6 publications

Literature Review of Research on Common Methods of Grapheme-To-Phoneme

Literature Review of Research on Common Methods of Grapheme-To-Phoneme

Novel two-stage model for grapheme-to-phoneme conversion using new grapheme generation rules

An Algerian dialect: Study and Resources

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