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arXiv · 2310.13812

Yet Another Model for Arabic Dialect Identification

Ajinkya Kulkarni·Hanan Aldarmaki
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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.

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Ajinkya Kulkarni, Hanan Aldarmaki. 2023-10-20. Yet Another Model for Arabic Dialect Identification. https://arxiv.org/abs/2310.13812

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