• Journal of Information Processing Systems
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• 제11권1호
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• pp.22-38
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• 2015

While Modern Standard Arabic is the formal spoken and written language of the Arab world; dialects are the major communication mode for everyday life. Therefore, identifying a speaker's dialect is critical in the Arabic-speaking world for speech processing tasks, such as automatic speech recognition or identification. In this paper, we examine two approaches that reduce the Universal Background Model (UBM) in the automatic dialect identification system across the five following Arabic Maghreb dialects: Moroccan, Tunisian, and 3 dialects of the western (Oranian), central (Algiersian), and eastern (Constantinian) regions of Algeria. We applied our approaches to the Maghreb dialect detection domain that contains a collection of 10-second utterances and we compared the performance precision gained against the dialect samples from a baseline GMM-UBM system and the ones from our own improved GMM-UBM system that uses a Reduced UBM algorithm. Our experiments show that our approaches significantly improve identification performance over purely acoustic features with an identification rate of 80.49%.

• International Journal of Computer Science & Network Security
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• 제24권10호
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• pp.43-52
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• 2024

A medium scale Urdu speakers' and English speakers' database with multiple accents and dialects has been developed to use in Urdu Speaker Verification Systems, English Speaker Verification Systems, accents and dialect verification systems. Urdu is the national language of Pakistan and English is the official language. Majority of the people are non-native Urdu speakers and non-native English in all regions of Pakistan in general and Gilgit-Baltistan region in particular. In order to design Urdu and English speaker verification systems for security applications in general and telephone banking in particular, two databases has been designed one for foreign accent of Urdu and another for foreign accent of English language. For the design of databases, voice data is collected from 180 speakers from GB region of Pakistan who could speak Urdu as well as English. The speakers include both genders (males and females) with different age groups ranging from 18 to 69 years. Finally, using a subset of the data, Multilayer Perceptron based speaker verification system has been designed. The designed system achieved overall accuracy rate of 83.4091% for English dataset and 80.0454% for Urdu dataset. It shows slight differences (4.0% with English and 7.4% with Urdu) in recognition accuracy if compared with the recently proposed multilayer perceptron (MLP) based SIS achieved 87.5% recognition accuracy