Statistical Analysis of Arabic Phonemes Used in Arabic Speech Recognition

Nahar, Khalid M.O.; Elshafei, Moustafa; Al-Khatib, Wasfi G.; Al-Muhtaseb, Husni; Alghamdi, Mohammed H.

doi:10.1007/978-3-642-34475-6_64

Cited by 8 publications

(8 citation statements)

References 3 publications

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“…A correlation analysis was also performed to determine if the frequency of occurrence of French or Arabic consonants influenced the accuracy of consonants’ production for that language (see Appendix). This frequency analysis used data on the frequency of consonants in adults in French from Malécot (1974) and Arabic from Nahar et al (2012). Finally, the speech pattern analysis focused on differences children produced compared to adult targets to document whether they used language-specific sounds (e.g., Arabic) in the productions of their other language (e.g., French).…”

Section: Methodsmentioning

confidence: 99%

Evidence of phonological transfer in bilingual preschoolers who speak Arabic and French

Meziane

MacLeod

2021

International Journal of Bilingualism

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Purpose: The purpose of this study is to document evidence of phonological transfer in bilingual preschoolers. Specifically, we focus on the phonological development of bilingual children acquiring French and Arabic. Method: Nine children who spoke Arabic and French, aged three years old, participated in a picture-naming task in both languages. Three types of measurement were used to examine the interaction between the two systems: consonants accuracy; inventory of acquired consonants; and types of errors. Data and analysis: The accuracy of shared and unshared consonants between Arabic and French were compared with a repeated measures analysis of variance, and error patterns were analyzed. Conclusions: Two main results suggest that these bilingual children have two phonological systems that interacted. First, the children had an inventory of acquired consonants that differed according to language. Second, they produced shared consonants more accurately than unshared consonants, indicating positive interactions and low accuracy for unshared consonants, indicating negative interactions. Originality: This study contributes much-needed research relating to the development of bilingual children, particularly children who are acquiring Arabic and French. Implications: These findings enrich the understanding of bilingual phonological development and contribute to understanding how two phonological systems interact. This study also has clinical implications for evaluation and intervention with bilingual children.

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Section: Methodsmentioning

confidence: 99%

Evidence of phonological transfer in bilingual preschoolers who speak Arabic and French

Meziane

MacLeod

2021

International Journal of Bilingualism

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“…Manner and place of articulation contribute significantly to complexity in all four languages, and not only in Arabic and French as predicted by H1.a. In other words, children in these languages do not systematically avoid fricatives or liquids, which are frequent in the ambient language (dos Santos 2007 for French, Nahar et al 2012 for Arabic), and to which they are consequently frequently exposed to via CDS. Similarly, for place of articulation, although children prefer labials and coronals to dorsals in the early period of production, they are able to produce posterior consonants (like dorsals and/or pharyngeals) when these types of sounds are frequently encountered in the ambient language (i.e., typically in Afro-Asiatic languages where they are rather frequent as shown by Basset (1946) and Bonnot (1977).…”

Section: Discussionmentioning

confidence: 99%

Development of phonetic complexity in Arabic, Berber, English and French

et al. 2018

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The goal of this study is to provide crosslinguistic data on the acquisition of phonetic complexity among children acquiring four different languages: Tunisian Arabic, Tashlhiyt Berber, English, and French. Using an adaptation of Jakielski's (2000) Index of Phonetic Complexity (IPC), we carried out an analysis to assess phonetic complexity of children's early vocabulary in the four languages. Four different samples from each language were analyzed: 50 words selected from an adult dictionary of each language, 50 words from child-directed speech, 50 words targeted by the child, and the child's actual pronunciations of those 50 words. Globally, we hypothesized that children's early productions would be shaped by universal articulatory constraints, but also by the language they are exposed to, depending on its phonological complexity. Our findings show that Arabic displays higher degrees of complexity compared to Berber, English and French, and that children acquiring Arabic target and produce more complex words than children learning Berber, English and French.

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“…The authors also focused on how to select optimum features from a 48-dimensional MDLF to increase the speaker recognition rate and the possibility of applying the MDLF features to Arabic speech recognition systems. In Arabic, some authors like the one in [21] suggested that knowing the length of a specific phoneme can assist in speeding up the recognition process as well as increasing recognition accuracy. As a result, the authors have presented a statistical study and analysis of the Arabic phonemes.…”

Section: Related Workmentioning

confidence: 99%

A Holy Quran Reader/Reciter Identification System Using Support Vector Machine

Nahar¹,

Al-Shannaq²,

Manasrah³

et al. 2019

IJMLC

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Holy Quran Reader Identification is the process of identifying the reader or reciter of the Holy Quran based on several features in the corresponding acoustic wave. In this research, we build our own corpus, which contains 15 known readers of the Holy Quran. The Mel-Frequency Cepstrum Coefficients (MFCC) are used for the extraction of these features from the input acoustic signal. These MFCCs are the reader's features matrix, which is used for recognition via Support Vector Machine (SVM) and Artificial Neural Networks (ANN). According to our experimental results, the Holy Quran Reader Identification System identifies the reader with 96.59% accuracy when using SVM, in contrast to accuracy of 86.1% when using ANN.

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Statistical Analysis of Arabic Phonemes Used in Arabic Speech Recognition

Cited by 8 publications

References 3 publications

Evidence of phonological transfer in bilingual preschoolers who speak Arabic and French

Evidence of phonological transfer in bilingual preschoolers who speak Arabic and French

Development of phonetic complexity in Arabic, Berber, English and French

A Holy Quran Reader/Reciter Identification System Using Support Vector Machine

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