Study of the Influence of Arabic Mother Tongue on the English Language Using a Hybrid Artificial Intelligence Method

Autor/inn/en	Mnasri, Sami; Habbash, Manssour
Titel	Study of the Influence of Arabic Mother Tongue on the English Language Using a Hybrid Artificial Intelligence Method
Quelle	In: Interactive Learning Environments, 31 (2023) 9, S.5568-5581 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mnasri, Sami)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2021.2012809
Schlagwörter	Arabic; Blended Learning; Artificial Intelligence; English (Second Language); Native Speakers; Dialects; Pronunciation; Foreign Countries; Reinforcement; Speech Communication; Electronic Learning; Technology Uses in Education; Saudi Arabia; Egypt; Tunisia + Suchen Sie Ihr Suchwort? Arabisch; Künstliche Intelligenz; English as second language; English; Second Language; Englisch als Zweitsprache; Muttersprachler; Dialect; Dialekt; Aussprache; Ausland; Positive Verstärkung; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Saudi-Arabien; Ägypten; Tunesien
Abstract	Accent recognition refers to the problem of inferring the native language of a speaker from his foreign-accented speech. Differences in accent are due to both articulation and prosodic characteristics. The automatic identification of foreign accents is valuable for different speech systems, such as speech recognition, speaker identification or voice conversion. This paper aims to identify the native languages of non-native English speakers from different countries in the Arabic region: the researchers choose Saudi Arabia to represent the eastern Arabic region in Asia, Egypt to represent the eastern Arabic region in Africa and Tunisia to represent the western Arabic region in Africa. In this research, reinforcement learning (RL), a sub-branch of machine learning, and artificial neural network will be used as an intelligent method to classify and predict the speech. The aim is to train a neural network to automatically detect speech accents. Then the researchers develop a hybrid multi-agent RL algorithm that takes advantage from the multi-agent communication and cooperative agents on the language detection process. Hence, the aim is to help sociolinguists and discourse analysts. As for the Saudi context, this study will be very useful on resolving e-learning issues such as linguistic problems of students. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01