Yet Another Model for Arabic Dialect Identification

Ajinkya Kulkarni, Hanan Aldarmaki


Abstract
In this paper, we describe a spoken Arabic dialect identification (ADI) model for Arabic that consistently outperforms previously published results on two benchmark datasets: ADI-5 and ADI-17. We explore two architectural variations: ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and features exratected from the pre-trained self-supervised model UniSpeech-SAT Large, as well as a fusion of all four variants. We find that individually, ECAPA-TDNN network outperforms ResNet, and models with UniSpeech-SAT features outperform models with MFCCs by a large margin. Furthermore, a fusion of all four variants consistently outperforms individual models. Our best models outperform previously reported results on both datasets, with accuracies of 84.7% and 96.9% on ADI-5 and ADI-17, respectively.
Anthology ID:
2023.arabicnlp-1.37
Volume:
Proceedings of ArabicNLP 2023
Month:
December
Year:
2023
Address:
Singapore (Hybrid)
Editors:
Hassan Sawaf, Samhaa El-Beltagy, Wajdi Zaghouani, Walid Magdy, Ahmed Abdelali, Nadi Tomeh, Ibrahim Abu Farha, Nizar Habash, Salam Khalifa, Amr Keleg, Hatem Haddad, Imed Zitouni, Khalil Mrini, Rawan Almatham
Venues:
ArabicNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
435–440
Language:
URL:
https://aclanthology.org/2023.arabicnlp-1.37
DOI:
10.18653/v1/2023.arabicnlp-1.37
Bibkey:
Cite (ACL):
Ajinkya Kulkarni and Hanan Aldarmaki. 2023. Yet Another Model for Arabic Dialect Identification. In Proceedings of ArabicNLP 2023, pages 435–440, Singapore (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Yet Another Model for Arabic Dialect Identification (Kulkarni & Aldarmaki, ArabicNLP-WS 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.arabicnlp-1.37.pdf