Name: ArPod
Description: 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.
Provider: nan
Paper Title: Building a Speech Corpus based on Arabic Podcasts for Language and Dialect Identification
Tasks: dialect classification from speech 
Venue Name: International Conference on Natural Language and Speech Processing
Authors: Khaled Lounnas, Mourad Abbas, Mohamed Lichouri
  
Abstract: 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
  