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Spoken Arabic dialect recognition using X-vectors

Published online by Cambridge University Press:  04 May 2020

Abualsoud Hanani  and
Rabee Naser
Abualsoud Hanani*
Affiliation:
Electrical and Computer Engineering, Birzeit University, Palestine
Rabee Naser
Affiliation:
Electrical and Computer Engineering, Birzeit University, Palestine
*
*Corresponding author. E-mail: abualsoudh@gmail.com
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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.

Keywords

X-vectorsArabic Dialect Recognition
Type
Article
Information
Natural Language Engineering , Volume 26 , Issue 6: Natural Language Processing for Similar Languages, Varieties, and Dialects , November 2020 , pp. 691 - 700
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Copyright
© Cambridge University Press 2020

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