Study of the influence of Arabic mother tongue on the English language using a hybrid artificial intelligence method

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.

KEYWORDS:

• Reinforcement learning
• Multi-agent systems
• Distributed Artificial Intelligence for real-world problems
• Accent detection
• Architectures for the educational technology system
• Simulations

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Notes on contributors

Sami Mnasri

Sami Mnasri received the bachelor’s degree in computer science from ENSIT University, Tunisia, the M.Sc. degree (Hons.) in computer science from Sfax University, Tunisia, in 2012 and the Ph.D. degree in computer science from UT2J University, Toulouse. Currently, he is a computer science assistant professor in the University of Tabuk. He published different high valued papers regarding the utility of using evolutionary optimization strategies such as genetic algorithms, particle swarms and ant colony on the resolution of real-world complex problems in the context of IoT networks and educational environments. He also achieved several researches on MAS and AI. He is the organizing co-chair of the IEEE International IINTEC conference and the WSDWSN international workshop. He was speaker in Saudiiot, IINTEC and HIS conferences.

Manssour Habbash

Manssour Habbash received his M.Sc. in educational research in 2006 from the University of Exceter, UK, and his Ph.D. in TESOL in 2012 from the same university. He is currently an associate professor and dean of the Community College, University of Tabuk, KSA (2017–Present). He was vice-dean and advisor in the same institute (2015–2017). He handled multiple responsibilities in the same university, such as vice-dean of academic affairs, head of the English department and deputy director in the English Language Center of the University of Tabuk. He is a reviewer in several journals. His research interests include the applied linguistics and teaching English to speakers of other languages.