Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/7872
Title: Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge
Authors: Naser, Rabee 
Hanani, Abualsoud 
Keywords: Computational linguistics;Speech processing systems;VarDial 2018
Issue Date: 2018
Publisher: COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of the 5th Workshop on NLP for Similar Languages, Varieties and Dialects, VarDial 2018
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 phone sequences 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%.
URI: http://hdl.handle.net/20.500.11889/7872
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