{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:31:42Z","timestamp":1776886302183,"version":"3.51.2"},"reference-count":26,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T00:00:00Z","timestamp":1588550400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Nat. Lang. Eng."],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper describes our automatic dialect identification system for recognizing four major Arabic dialects, as well as Modern Standard Arabic. We adapted the X-vector framework, which was originally developed for speaker recognition, to the task of Arabic dialect identification (ADI). The training and development ADI VarDial 2018 and VarDial 2017 were used to train and test all of our ADI systems. In addition to the introduced X-vectors, other systems use the traditional i-vectors, bottleneck features, phonetic features, words transcriptions, and GMM-tokens. X-vectors achieved good performance (0.687) on the ADI 2018 Discriminating between Similar Languages shared task testing dataset, outperforming other systems. The performance of the X-vector system is slightly improved (0.697) when fused with i-vectors, bottleneck features, and word uni-gram features.<\/jats:p>","DOI":"10.1017\/s1351324920000091","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T09:54:16Z","timestamp":1588586056000},"page":"691-700","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":15,"title":["Spoken Arabic dialect recognition using X-vectors"],"prefix":"10.1017","volume":"26","author":[{"given":"Abualsoud","family":"Hanani","sequence":"first","affiliation":[]},{"given":"Rabee","family":"Naser","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2020,5,4]]},"reference":[{"key":"S1351324920000091_ref11","first-page":"1871","article-title":"Liblinear: A library for large linear classification","volume":"9","author":"Fan","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"S1351324920000091_ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-1211"},{"key":"S1351324920000091_ref18","unstructured":"Povey, D. , Ghoshal, A. , Boulianne, G. , Burget, L. , Glembek, O. , Goel, N. , Hannemann, M. , Motlicek, P. , Qian, Y. , Schwarz, P. , et al. 2011. The Kaldi speech recognition toolkit. In IEEE 2011 Workshop on Automatic Speech Recognition and Understanding, Number EPFL-CONF-192584. IEEE Signal Processing Society."},{"key":"S1351324920000091_ref23","doi-asserted-by":"crossref","unstructured":"T\u00fcske, Z. , Golik, P. , Schl\u00fcter, R. and Ney, H. (2014). Acoustic modeling with deep neural networks using raw time signal for LVCSR. 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Automatic dialect detection in arabic broadcast speech. arXiv preprint arXiv:1509.06928."},{"key":"S1351324920000091_ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2012.01.003"},{"key":"S1351324920000091_ref24","doi-asserted-by":"crossref","unstructured":"Wray, S. and Ali, A. (2015). Crowdsource a little to label a lot: Labeling a speech corpus of dialectal arabic. In Sixteenth Annual Conference of the International Speech Communication Association.","DOI":"10.21437\/Interspeech.2015-594"},{"key":"S1351324920000091_ref16","unstructured":"Malmasi, S. , Zampieri, M. , Ljube\u0161i\u0107, N. , Nakov, P. , Ali, A. and Tiedemann, J. (2016). Discriminating between similar languages and arabic dialect identification: A report on the third DSL shared task. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pp. 1\u201314."},{"key":"S1351324920000091_ref10","unstructured":"Elfardy, H. and Diab, M. (2013). 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