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. 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. We investigate different approaches for dialect identification in Arabic broadcast speech, using phonetic, lexical features obtained from a speech recognition system, and acoustic features using the i-vector framework. We studied both generative and discriminate classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic English language identification task, with an accuracy of 100 . We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100 . We further report results using the proposed method to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 52 . We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. We also release the train and test data as standard corpus for dialect identification. We propose a new blind segmentation approach to acoustic event detection (AED) based on i-vectors. Conventional approaches to AED often required well-segmented data with non-overlapping boundaries for competing events. Inspired by block-based automatic image annotation in image retrieval tasks, we blindly segment audio streams into equal-length pieces, label the underlying observed acoustic events with multiple categories and with no event boundary information, extract i-vector for them, and perform classification using support vector machine and maximal figure-of-merit based classifiers. Experiments on various sets of audio data show promising results with an average of 8 absolute gain in F1 over the conventional hidden Markov model based approach. An enhanced robustness at different noise levels is also observed. The key to the success lies in the enhanced discrimination power offered by the i-vector representation of the acoustic data. Index Terms: acoustic event detection, i-vector, blind segmentation, support vector machine, maximal figure-of-merit