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 , 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.
We describe a new approach to automatic dialect and accent recognition which exceeds state-of-the-art performance in three recognition tasks. This approach improves the accuracy and substantially lower the time complexity of our earlier phoneticbased kernel approach for dialect recognition. In contrast to state-of-the-art acoustic-based systems, our approach employs phone labels and segmentation to constrain the acoustic models. Given a speaker’s utterance, we first obtain phone hypotheses using a phone recognizer and then extract GMM-supervectors for each phone type, effectively summarizing the speaker’s phonetic characteristics in a single vector of phone-type supervectors. Using these vectors, we design a kernel function that computes the phonetic similarities between pairs of utterances to train SVM classifiers to identify dialects. Comparing this approach to the state-of-the-art, we obtain a 12.9 relative improvement in EER on Arabic dialects, and a 17.9 relative improvement for American vs. Indian English dialects. We also see a 53.5 relative improvement over a GMM-UBM on American Southern vs. Non-Southern English.