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.
Another work on ADID has been proposed by @cite_20 , which combined the prosodic and phonotactic approaches. In fact, they augmented their phonotactic system, described above, by adding some prosodic features like durations and fundamental frequency measured at n-gram level where grams are syllables. They tested their system on four Arabic dialects: Gulf, Iraqi, Levantine, and Egyptian.
@cite_20
2248508985 1657486103 2183209506 2068056889
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. While Modern Standard Arabic is the formal spoken and written language of the Arab world, dialects are the major communication mode for everyday life; identifying a speaker’s dialect is thus critical to speech processing tasks such as automatic speech recognition, as well as speaker identification. We examine the role of prosodic features (intonation and rhythm) across four Arabic dialects: Gulf, Iraqi, Levantine, and Egyptian, for the purpose of automatic dialect identification. We show that prosodic features can significantly improve identification, over a purely phonotactic-based approach, with an identification accuracy of 86.33 for 2m utterances. 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. Conversion of silent articulation captured by ultrasound and video to modal speech.Comparison of GMM and full-covariance phonetic HMM without vocabulary limitation.HMM-based approach allows the use of linguistic information for regularization.Objective evaluation showed a lower but more fluctuant spectral distortion for HMM.Perceptual evaluation showed a better intelligibility for HMM on consonants. This article investigates the use of statistical mapping techniques for the conversion of articulatory movements into audible speech with no restriction on the vocabulary, in the context of a silent speech interface driven by ultrasound and video imaging. As a baseline, we first evaluated the GMM-based mapping considering dynamic features, proposed by (2007) for voice conversion. Then, we proposed a 'phonetically-informed' version of this technique, based on full-covariance HMM. This approach aims (1) at modeling explicitly the articulatory timing for each phonetic class, and (2) at exploiting linguistic knowledge to regularize the problem of silent speech conversion. Both techniques were compared on continuous speech, for two French speakers (one male, one female). For modal speech, the HMM-based technique showed a lower spectral distortion (objective evaluation). However, perceptual tests (transcription and XAB discrimination tests) showed a better intelligibility of the GMM-based technique, probably related to its less fluctuant quality. For silent speech, a perceptual identification test revealed a better segmental intelligibility for the HMM-based technique on consonants.