Arabic Automatic Speech Recognition: A Systematic Literature Review

Amira Dhouib, Achraf Othman, Oussama El Ghoul, Mohamed Koutheair Khribi, Aisha Al Sinani

Journal: MDPI Applied Sciences | Special Issue: Automatic Speech Recognition | Journal Rank: Q2 | Impact Factor 2020: 2.679 | 2022

Automatic Speech Recognition (ASR), also known as Speech-To-Text (STT) or computer speech recognition, has been an active field of research recently. This study aims to chart this field by performing a Systematic Literature Review (SLR) to give insight into the ASR studies proposed, especially for the Arabic language. The purpose is to highlight the trends of research about Arabic ASR and guide researchers with the most significant studies published over ten years from 2011 to 2021. This SLR attempts to tackle seven specific research questions related to the toolkits used for developing and evaluating Arabic ASR, the supported type of the Arabic language, the used feature extraction/classification techniques, the type of speech recognition, the performance of Arabic ASR, the existing gaps facing researchers, along with some future research. Across five databases, 38 studies met our defined inclusion criteria. Our results showed different open-source toolkits to support Arabic speech recognition. The most prominent ones were KALDI, HTK, then CMU Sphinx toolkits. A total of 89.47% of the retained studies cover modern standard Arabic, whereas 26.32% of them were dedicated to different dialects of Arabic. MFCC and HMM were presented as the most used feature extraction and classification techniques, respectively: 63% of the papers were based on MFCC and 21% were based on HMM. The review also shows that the performance of Arabic ASR systems depends mainly on different criteria related to the availability of resources, the techniques used for acoustic modeling, and the used datasets.