ArPod
The corpus has a duration of 8.1 hours sampled at 16 kHz and coded on 16 bits. The considered languages are: MSA, English; whereas the dialects are: Syrian, Saudi Arabic, Egyptian, and Lebanese. 70% of this dataset had been used for training, and the remaining for the test phase.
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Building a Speech Corpus based on Arabic Podcasts for Language and Dialect Identification
dialect classification from speech 
International Conference on Natural Language and Speech Processing
Khaled Lounnas, Mourad Abbas, Mohamed Lichouri

In this paper, we present ArPod, a new Arabic speech
corpus made of Arabic audio podcasts. We built this
dataset, mainly for both speech-based multi-lingual and
multi-dialectal identification tasks. It includes two languages: Modern Standard Arabic (MSA) and English,
and four Arabic dialects: Saudi, Egyptian, Lebanese
and Syrian. A set of supervised classifiers have been
used: Support Vector Machines (SVM), Multi Layer
Perceptron (MLP), K-Nearest Neighbors (KNN), Extratrees and Convolutional Neural Networks (CNN),
using acoustic and spectral features. For both tasks,
SVM yielded encouraging results and outperformed the
other classifiers. Language Identification, Dialect
Identification, CNN, Acoustic features, spectral features, SVM, Arabic Podcast
