  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.
