Dialect classification using acoustic and linguistic features in Arabic speech

Mohammad Ali Humayun, Hayati Yassin, Pg Emeroylariffion Abas


Speech dialects refer to linguistic and pronunciation variations in the speech of the same language. Automatic dialect classification requires considerable acoustic and linguistic differences between different dialect categories of speech. This paper proposes a classification model composed of a combination of classifiers for the Arabic dialects by utilizing both the acoustic and linguistic features of spontaneous speech. The acoustic classification comprises of an ensemble of classifiers focusing on different frequency ranges within the short-term spectral features, as well as a classifier utilizing the ‘i-vector’, whilst the linguistic classifiers use features extracted by transformer models pre-trained on large Arabic text datasets. It has been shown that the proposed fusion of multiple classifiers achieves a classification accuracy of 82.44% for the identification task of five Arabic dialects. This represents the highest accuracy reported on the dataset, despite the relative simplicity of the proposed model, and has shown its applicability and relevance for dialect identification tasks. 


Acoustic features; Arabic language; Dialect identification; Linguistic features; Spontaneous speech

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DOI: http://doi.org/10.11591/ijai.v12.i2.pp739-746


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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