  In daily communications, Arabs use local dialects which are hard to identify
automatically using conventional classification methods. The dialect
identification challenging task becomes more complicated when dealing with an
under-resourced dialects belonging to a same county/region. In this paper, we
start by analyzing statistically Algerian dialects in order to capture their
specificities related to prosody information which are extracted at utterance
level after a coarse-grained consonant/vowel segmentation. According to these
analysis findings, we propose a Hierarchical classification approach for spoken
Arabic algerian Dialect IDentification (HADID). It takes advantage from the
fact that dialects have an inherent property of naturally structured into
hierarchy. Within HADID, a top-down hierarchical classification is applied, in
which we use Deep Neural Networks (DNNs) method to build a local classifier for
every parent node into the hierarchy dialect structure. Our framework is
implemented and evaluated on Algerian Arabic dialects corpus. Whereas, the
hierarchy dialect structure is deduced from historic and linguistic knowledges.
The results reveal that within {\HD}, the best classifier is DNNs compared to
Support Vector Machine. In addition, compared with a baseline Flat
classification system, our HADID gives an improvement of 63.5% in term of
precision. Furthermore, overall results evidence the suitability of our
prosody-based HADID for speaker independent dialect identification while
requiring less than 6s test utterances.
