Effective Arabic dialect classification using diverse phonotactic models
Murat Akbacak, Dimitra Vergyri, Andreas Stolcke, Nicolas Scheffer, Arindam Mandal · 2011
We study the effectiveness of recently developed language recognition techniques based on speech recognition models for the discrimination of Arabic dialects. Specifically, we investi-gate dialect-specific and cross-dialectal phonotactic models, us-ing 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 mod-eling paradigms, and achieve 2 % average equal error rate for pairwise classification. 1.