Spoken Arabic dialect recognition using X-vectors. (4th November 2020)

• Record Type: Journal Article
• Authors: Hanani, Abualsoud; Naser, Rabee
• Editors: Zampieri, Marcos; Nakov, Preslav
• Is Part Of: Natural language engineering. Volume 26:Part 6(2020)
• Page Start: 691
• Page End: 700
• Publication Date: 2020-11-04
• Subjects: X-vectors, -- Arabic Dialect Recognition; Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324920000091
• Languages: English
• ISSNs: 1351-3249

Abstract

Abstract: 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.