TY - GEN
T1 - AI-based Arabic Language and Speech Tutor
AU - Shao, Sicong
AU - Alharir, Saleem
AU - Hariri, Salim
AU - Satam, Pratik
AU - Shiri, Sonia
AU - Mbarki, Abdessamad
N1 - Funding Information:
1 The courses were prepared in collaboration with the Language Flagship Technology Innovation Center at the University of Hawai`i at Manoa with funding from the National Security Education Program.
Funding Information:
ACKNOWLEDGMENT This work is partly supported by National Science Foundation (NSF) research projects NSF-1624668 and NSF-1849113, (NSF) DUE-1303362 (Scholarship-for-Service), and Department of Energy/National Nuclear Security Administration under Award Number(s) DE-NA0003946.
Funding Information:
This work is partly supported by National Science Foundation (NSF) research projects NSF-1624668 and NSF- 1849113, (NSF) DUE-1303362 (Scholarship-for-Service), and Department of Energy/National Nuclear Security Administration under Award Number(s) DE-NA0003946.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the past decade, we have observed a growing interest in using technologies such as artificial intelligence (AI), machine learning, and chatbots to provide assistance to language learners, especially in second language learning. By using AI and natural language processing (NLP) and chatbots, we can create an intelligent self-learning environment that goes beyond multiple-choice questions and/or fill in the blank exercises. In addition, NLP allows for learning to be adaptive in that it offers more than an indication that an error has occurred. It also provides a description of the error, uses linguistic analysis to isolate the source of the error, and then suggests additional drills to achieve optimal individualized learning outcomes. In this paper, we present our approach for developing an Artificial Intelligence-based Arabic Language and Speech Tutor (AI-ALST) for teaching the Moroccan Arabic dialect. The AI-ALST system is an intelligent tutor that provides analysis and assessment of students learning the Moroccan dialect at University of Arizona (UA). The AI-ALST provides a self-learned environment to practice each lesson for pronunciation training. In this paper, we present our initial experimental evaluation of the AI-ALST that is based on MFCC (Mel frequency cepstrum coefficient) feature extraction, bidirectional LSTM (Long Short-Term Memory), attention mechanism, and a cost-based strategy for dealing with class-imbalance learning. We evaluated our tutor on the word pronunciation of lesson 1 of the Moroccan Arabic dialect class. The experimental results show that the AI-ALST can effectively and successfully detect pronunciation errors and evaluate its performance by using F_1 - score, accuracy, precision, and recall.
AB - In the past decade, we have observed a growing interest in using technologies such as artificial intelligence (AI), machine learning, and chatbots to provide assistance to language learners, especially in second language learning. By using AI and natural language processing (NLP) and chatbots, we can create an intelligent self-learning environment that goes beyond multiple-choice questions and/or fill in the blank exercises. In addition, NLP allows for learning to be adaptive in that it offers more than an indication that an error has occurred. It also provides a description of the error, uses linguistic analysis to isolate the source of the error, and then suggests additional drills to achieve optimal individualized learning outcomes. In this paper, we present our approach for developing an Artificial Intelligence-based Arabic Language and Speech Tutor (AI-ALST) for teaching the Moroccan Arabic dialect. The AI-ALST system is an intelligent tutor that provides analysis and assessment of students learning the Moroccan dialect at University of Arizona (UA). The AI-ALST provides a self-learned environment to practice each lesson for pronunciation training. In this paper, we present our initial experimental evaluation of the AI-ALST that is based on MFCC (Mel frequency cepstrum coefficient) feature extraction, bidirectional LSTM (Long Short-Term Memory), attention mechanism, and a cost-based strategy for dealing with class-imbalance learning. We evaluated our tutor on the word pronunciation of lesson 1 of the Moroccan Arabic dialect class. The experimental results show that the AI-ALST can effectively and successfully detect pronunciation errors and evaluate its performance by using F_1 - score, accuracy, precision, and recall.
KW - attention mechanism
KW - Automatic speech recognition
KW - computer-assisted second language learning
KW - deep learning
KW - LSTM
UR - http://www.scopus.com/inward/record.url?scp=85147042595&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147042595&partnerID=8YFLogxK
U2 - 10.1109/AICCSA56895.2022.10017924
DO - 10.1109/AICCSA56895.2022.10017924
M3 - Conference contribution
AN - SCOPUS:85147042595
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications, AICCSA 2022 - Proceedings
PB - IEEE Computer Society
T2 - 19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022
Y2 - 5 December 2022 through 7 December 2022
ER -