12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Effective Arabic Dialect Classification Using Diverse Phonotactic Models

Murat Akbacak (1), Dimitra Vergyri (1), Andreas Stolcke (2), Nicolas Scheffer (1), Arindam Mandal (1)

(1) SRI International, USA
(2) Microsoft Speech Labs, USA

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.

Full Paper

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.