D. A. Reynolds, W. M. Campbell, W. Shen, and E. Singer, “Automatic language recognition via spectral and token based approaches,” in Springer Handbook of Speech Processing, J. Benesty, M. M. Sondhi, and Y. Huang, Eds. Springer, 2008.
 E. Ambikairajah, H. Li, L. Wang, B. Yin, and V. Sethu, “Language identification: A tutorial,” Circuits and Systems Magazine, IEEE, vol. 11, no. 2, pp. 82-108.
 M. Zissman, “Comparison of four approaches to automatic language identification of telephone speech,” IEEE Transactions on Speech and Audio Processing, vol. 4, no. 1, pp. 31-44, 1996.
 D. Mart´ınez, L. Burget, L. Ferrer, and N. Scheffer, “ivector-based prosodic system for language identification,” in ICASSP, 2012, pp. 4861-4864.
 O. Plchot, M. Diez, M. Soufifar, and L. Burget, “Pllr features in language recognition system for rats,” in Fifteenth Annual Conference of the International Speech Communication Association, 2014.
 M. H. Bahari, N. Dehak, L. Burget, A. Ali, J. Glass et al., “Nonnegative factor analysis for gmm weight adaptation,” IEEE Transactions on Audio Speech and Language Processing, 2014.
 H. Soltau, L. Mangu, and F. Biadsy, “From modern standard arabic to levantine asr: Leveraging gale for dialects,” in ASRU, 2011, pp. 266-271. [OpenAIRE]
 M. Soufifar, S. Cumani, L. Burget, and J. Cˇernocky, “Discriminative classifiers for phonotactic language recognition with ivectors,” in ICASSP, 2012, pp. 4853-4856.
 A. Ali, Y. Zhang, and S. Vogel, “QCRI advanced transcription ssystem (QATS),” in SLT, 2014.
 S. Wray and A. Ali, “Crowdsource a little to label a lot: Labeling a speech corpus of dialectal arabic,” in INTERSPEECH, 2015.
In this paper, we investigate different approaches for dialect identification in Arabic broadcast speech. These methods are based on phonetic and lexical features obtained from a speech recognition system, and bottleneck features using the i-vector framework. We studied both generative and discriminative classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We also evaluated these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further reported results using the proposed methods to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 59.2%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. All the data used on our experiments have been released to the public as a language identification corpus.