We study the effectiveness of recently developed language recognition techniques based on speech recognition models for the discrimination of Arabic dialects. Specifically, we investigate dialect-specific and cross-dialectal phonotactic models, using both language models and support vector machines (SVMs). Techniques are evaluated both alone and in combination with a cepstral system with joint factor analysis (JFA), using a four-dialect data set employing 30-second telephone speech samples. We find good complementarity from different features and modeling paradigms, and achieve 2% average equal error rate for pairwise classification.
Bibliographic reference. Akbacak, Murat / Vergyri, Dimitra / Stolcke, Andreas / Scheffer, Nicolas / Mandal, Arindam (2011): "Effective Arabic dialect classification using diverse phonotactic models", In INTERSPEECH-2011, 737-740.