AI-based Arabic Language and Speech Tutor

Sicong Shao, Saleem Alharir, Salim Hariri, Pratik Satam, Sonia Shiri, Abdessamad Mbarki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2022 IEEE/ACS 19th International Conference on Computer Systems and Applications, AICCSA 2022 - Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798350310085
DOIs
StatePublished - 2022
Event19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022 -
Duration: Jan 1 2022 → …

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume2022-December

Conference

Conference19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022
Period1/1/22 → …

Keywords

  • Automatic speech recognition
  • LSTM
  • attention mechanism
  • computer-assisted second language learning
  • deep learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'AI-based Arabic Language and Speech Tutor'. Together they form a unique fingerprint.

Cite this