# Arabic Automatic Speech Recognition: A Systematic Literature Review
## 5 Conclusions  
This paper aims to compile the existing scientific knowledge about Arabic ASR studies published between 2011 and 2021. For that, a systematic review of the literature is conducted. A total of 38 conferences, workshops, and articles papers were reviewed from five academic databases: Google Scholar, IEEE Xplore, Science Direct, ACM Digital Library, and Springer Link. Our results and discussion revolve around seven fundamental research questions. The purposes were to provide insight into the used toolkits for implementing \begin{table}
\begin{tabular}{c c} \hline \hline
**References** & **Performance** \\ \hline
[51] & The WER scored 76.4\% for the non-diacritized \\  & text system and 63.8\% for the diacritized text-based system. \\ \hline
[52] & The obtained accuracy was 90.18\%. \\ \hline
[53] & The best performance achieved was 24.4\% of WER. \\ \hline
[54] & The experimental results show that the non-diacritized text system scored 81.2\%, while the diacritized text-based \\  & system scored 69.1\%. \\ \hline
[55] & The achieved result is 31.10\% WER using the standard CTC-attention method. For CNN-LSTM with the attention method, the best result is obtained from this model: 28.48\% as WER. \\ \hline
[56] & Accuracy when using GFC with CNN was 99.77\%. \\ \hline
[57] & The result WER obtained on MGB-3 using a 4-g re-scoring strategy is 42.25\% for a BLSTM system, compared to 65.44\% for a DNN system. \\ \hline
[58] & For TV commands, accuracy is over 95\% for all models. \\ \hline
[11] & The WER is much higher on the dialectal Arabic dataset, ranging from 40\% to 50\%. \\  & The WER for the proposed ASR system using all five references achieved a score of 25.3\%. \\ \hline
[59] & The final system was a combination of five systems where the result obtained succeeded the best single LIUM ASR system with a 9\% of WER reduction and succeeded the baseline MGB system that the organizers provided with a 43\% WER reduction. \\ \hline
[19] & The system accuracy reached 94.56\%. \\ \hline
[60] & The recognition rate of trained data reached 97.8\%. \\  & The recognition rate of non-trained data reached 81.1\%. \\ \hline
[61] & The proposed ASR achieved a 28.9\% relative reduction in WER. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._and evaluating Arabic ASR, the variants of the Arabic language, the used feature extraction and classification techniques, the performance of Arabic ASR systems, the type of speech recognition, the existing gaps, and future research in the Arabic ASR field. Our findings illustrate that this is still an emerging research area where the number of studies has increased over the years. Many studies focus on MSA, whereas a relatively limited number of papers concentrate on dialectal Arabic. Some of these papers were dedicated to more than one variant of Arabic, and the reviewed studies did not support different dialects of the Arabic language. It would be interesting to focus on DA and use common datasets for dialect speech to enhance this research area gradually. Many toolkits were used to build and assess Arabic ASR. The most prominent ones were KALDI, HTK, and CMU Sphinx open-source toolkits. Concerning the used feature extraction techniques, MFCC was the most used one, followed by LDA, then PLP, and LPC. The results show also that HMM was the most adopted classification technique in the reviewed studies. Different limitations have also been addressed in the reviewed studies. The pronunciation variation phenomenon and the low availability of common large diacritized text for the Arabic language can be considered significant challenges that might limit research in this field. It would be interesting then to focus on the non-diacritized Arabic scripts and to develop more large and common datasets for both MSA and dialectal Arabic. Conceptualization, A.D. and A.O. ; methodology, A.D. and A.O. ; software, A.D., A.O. and O.E.G. ; validation, A.D., A.O. and O.E.G.