# Arabic Automatic Speech Recognition: A Systematic Literature Review
###### Abstract  
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. A 2022 2021 20  
used language worldwide [5] and is used by more than 422 million native and non-native people [6; 7]. According to [8], the Arabic language can be classified according to three primary types, namely:  
* Classical Arabic represents the most formal and standard form of Arabic as it is mainly used in the Holy Quran and the religious instructions of Islam [1];
* Modern Standard Arabic (MSA) represents the current formal linguistic standard of the Arabic language. It is generally used in written communication and media, and is taught in educational institutions [1];
* Dialectal Arabic (DA), also called colloquial Arabic, is a variation of the same language specific to countries or social groups used in everyday life. Various dialects of Arabic exist, and, sometimes, more than one DA can be used within a country [1]. Based on the study of [PERSON] et al. [6], the DA can be categorized according to the following varieties: 1. North Africa, which includes the Tunisian, Algerian, Moroccan, Mauritanian, and Libyan dialects; 2. Gulf dialect, which includes Qatari, Kuwait, Saudi, Omani, Bahraini, and Emirati dialects; 3. Nile Basin, including the Egyptian and Sudanese dialects; 4. Yemeni dialect; 5. Iraqi dialect; 6. Levantine dialect is often used in Syria, Lebanon, Palestine, and western Jordan [9]. Compared to the existing research on ASR for the English language, the ASR for the Arabic language received little attention due to its consideration as a limited resource language [10]. The main challenges of the Arabic language remain specific to the existence of enormous dialects with various pronunciations, the morphological complexity, and the difficulty of acquiring a diacritized transcription of the speech corpora, which is not very commonly open-source, etc. [11]. Toward building a robust Arabic ASR system, it is highly recommended and more accurate to use extensive speech collections. According to [12], an extensive vocabulary means that the dataset contains approximately 20k to 60k words. Several overviews and survey studies have been published to review various aspects of Arabic speech recognition. In 2018, the authors of [13] published a literature survey paper that discusses the Arabic ASR. The survey shows that few freely available continuous speech corpora exist. It also shows a need to compile large corpora. In another study, [PERSON] et al.