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.
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