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.
In contrast of the other acoustic phonetic approaches, only @cite_52 and @cite_38 have proposed ADID system for Intra-country context. @cite_38 have investigated an acoustic approach based on i-vectors method for regional accents recognition. They performed their experiments on Arabic Palestinian accents from four different regions: Jerusalem, Hebron, Nablus and Ramallah. Whereas, @cite_52 designed a GMM-UBM and an i-vectors framework for accent recognition. They implement their experiments on a selected data spoken in three Algerian ares, which are the East, Center and West of Algeria.
@cite_38 @cite_52
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We attempt to automatically recognize the speaker's accent among regional Arabic Palestinian accents from four different regions of Palestine, i.e. Jerusalem (JE), Hebron (HE), Nablus (NA) and Ramallah (RA). To achieve this goal, we applied the state of the art techniques used in speaker and language identification, namely, Gaussian Mixture Model - Universal Background Model (GMM-UBM), Gaussian Mixture Model - Support Vector Machines (GMM-SVM) and I-vector framework. All of these systems were trained and tested on speech of 200 speakers. GMM-SVM and I-vector systems outperformed the baseline GMM-UBM system. The best result (accuracy of 81.5 ) was obtained by an I-vector system with 64 Gaussian components, compared to an accuracy of 73.4 achieved by human listeners on the same testing utterances. Volume I: An Introduction: Preface Typographical conventions and phonetic symbols Part I. Aspects of Accent: 1. Linguistic and social variability 2. Accent phonology 3. How accents differ 4. Why accents differ Part II. Sets and Systems: 5. The reference accents 6. Standard lexical sets 7. Systems: a typology Part III. Developments and Processes: 8. Residualisms 9. British prestige innovations 10. Some American innovations 11. Some further British innovations Sources and further reading References Index.