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        <title>Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge</title>
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            <title>Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)</title>
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            <namePart type="given">Marcos</namePart>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
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        <name type="personal">
            <namePart type="given">Nikola</namePart>
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    <abstract>This paper describes our Automatic Dialect Recognition (ADI) system for the VarDial 2018 challenge, with the goal of distinguishing four major Arabic dialects, as well as Modern Standard Arabic (MSA). The training and development ADI VarDial 2018 data consists of 16,157 utterances, their words transcription, their phonetic transcriptions obtained with four non-Arabic phoneme recognizers and acoustic embedding data. Our overall system is a combination of four different systems. One system uses the words transcriptions and tries to recognize the speaker dialect by modeling the sequence of words for each dialect. Another system tries to recognize the dialect by modeling the phones sequence produced by non-Arabic phone recognizers, whereas, the other two systems use GMM trained on the acoustic features for recognizing the dialect. The best performance was achieved by the fused system which combines four systems together, with F1 micro of 68.77%.</abstract>
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%0 Conference Proceedings
%T Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge
%A Naser, Rabee
%A Hanani, Abualsoud
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shervin
%Y Ali, Ahmed
%S Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F naser-hanani-2018-birzeit
%X This paper describes our Automatic Dialect Recognition (ADI) system for the VarDial 2018 challenge, with the goal of distinguishing four major Arabic dialects, as well as Modern Standard Arabic (MSA). The training and development ADI VarDial 2018 data consists of 16,157 utterances, their words transcription, their phonetic transcriptions obtained with four non-Arabic phoneme recognizers and acoustic embedding data. Our overall system is a combination of four different systems. One system uses the words transcriptions and tries to recognize the speaker dialect by modeling the sequence of words for each dialect. Another system tries to recognize the dialect by modeling the phones sequence produced by non-Arabic phone recognizers, whereas, the other two systems use GMM trained on the acoustic features for recognizing the dialect. The best performance was achieved by the fused system which combines four systems together, with F1 micro of 68.77%.
%U https://aclanthology.org/W18-3924
%P 210-217

Markdown (Informal)

[Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge](https://aclanthology.org/W18-3924) (Naser & Hanani, VarDial 2018)

• Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge (Naser & Hanani, VarDial 2018)

ACL

• Rabee Naser and Abualsoud Hanani. 2018. Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), pages 210–217, Santa Fe, New Mexico, USA. Association for Computational Linguistics.