Effective Arabic dialect classification using diverse phonotactic models


M. Akbacak, D. Vergyri, A. Stolcke, N. Scheffer and A. Mandal, “Effective Arabic dialect classification using diverse phonotactic models,” in Proc. Interspeech, 2011, pp. 141–144.


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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