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In daily communications, Arabs use local dialects which are hard to identifyautomatically using conventional classification methods. The dialectidentification challenging task becomes more complicated when dealing with anunder-resourced dialects belonging to a same county/region. In this paper, westart by analyzing statistically Algerian dialects in order to capture theirspecificities related to prosody information which are extracted at utterancelevel after a coarse-grained consonant/vowel segmentation. According to theseanalysis findings, we propose a Hierarchical classification approach for spokenArabic algerian Dialect IDentification (HADID). It takes advantage from thefact that dialects have an inherent property of naturally structured intohierarchy. Within HADID, a top-down hierarchical classification is applied, inwhich we use Deep Neural Networks (DNNs) method to build a local classifier forevery parent node into the hierarchy dialect structure. Our framework isimplemented and evaluated on Algerian Arabic dialects corpus. Whereas, thehierarchy dialect structure is deduced from historic and linguistic knowledges.The results reveal that within {\HD}, the best classifier is DNNs compared toSupport Vector Machine. In addition, compared with a baseline Flatclassification system, our HADID gives an improvement of 63.5% in term ofprecision. Furthermore, overall results evidence the suitability of ourprosody-based HADID for speaker independent dialect identification whilerequiring less than 6s test utterances.

Hierarchical Classification for Spoken Arabic Dialect Identification using Prosody: Case of Algerian Dialects