Name: QAC: Qatari Arabic Corpus
Description: Speech was recorded from four Qatari television programs in 2009-2011:
Provider: Qatar University
Paper Title: Development of a TV Broadcasts Speech Recognition System for Qatari Arabic
Tasks: speech recognition 
Venue Name: Language Resources and Evaluation Conference
Authors: Mohamed Elmahdy, Mark Hasegawa-Johnson, Eiman Mustafawi
Abstract: A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, a transfer learning
  framework is proposed to jointly use a large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to
  improve acoustic and language modeling. The Qatari Arabic (QA) dialect has been chosen as a typical example for an under-resourced
  Arabic dialect. A wide-band speech corpus has been collected and transcribed from several Qatari TV series and talk-show programs.
  A large vocabulary speech recognition baseline system was built using the QA corpus. The proposed MSA-based transfer learning
  technique was performed by applying orthographic normalization, phone mapping, data pooling, acoustic model adaptation, and system
  combination. The proposed approach can achieve more than 28% relative reduction in WER.
  