@InProceedings{menacer-EtAl:2017:W17-13,
  author    = {Menacer, Mohamed Amine  and  Mella, Odile  and  Fohr, Dominique  and  Jouvet, Denis  and  Langlois, David  and  Smaili, Kamel},
  title     = {An enhanced automatic speech recognition system for Arabic},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {157--165},
  abstract  = {Automatic speech recognition for Arabic
	is a very challenging task. Despite all the
	classical techniques for Automatic Speech
	Recognition (ASR), which can be efficiently applied to Arabic speech
	recognition, it is essential to take into consideration
	the language specificities to improve
	the system performance. In this article, we
	focus on Modern Standard Arabic (MSA)
	speech recognition. We introduce the challenges
	related to Arabic language, namely
	the complex morphology nature of the language
	and the absence of the short vowels
	in written text, which leads to several potential
	vowelization for each graphemes,
	which is often conflicting. We develop
	an ASR system for MSA by using Kaldi
	toolkit. Several acoustic and language
	models are trained. We obtain a Word Error
	Rate (WER) of 14.42 for the baseline
	system and 12.2 relative improvement by
	rescoring the lattice and by rewriting the
	output with the right Z hamoza above or
	below Alif.},
  url       = {http://www.aclweb.org/anthology/W17-1319}
}