# Arabic Automatic Speech Recognition: A Systematic Literature Review
## 2 Background  
Automatic speech recognition concerns the automated conversion of speech or audio waves into texts exploitable by a machine through analyzing and processing speech signals using different techniques such as Convolutional Neural Network (CNN) [16] or deep learning [17]. The design of an ASR architecture system depends on various components and tasks like preprocessing, noise detection, speech classification, and feature extraction. Figure 1 presents a generic architecture used in the development of ASR systems. Three main modules can be identified in a traditional speech recognition system [4]. The first one corresponds to speech pre-processing, which aims to remove undesirable noises from the speech signal and identify speech activity [18]. The second module concerns feature extraction, in which essential data are extracted from a speech. The third module refers to the classification, which aims to find the parameter set from memory.  
The critical challenge in developing highly accurate Arabic ASR systems is selecting feature extraction and classification techniques [19]. Mel Frequency Cepstral Coefficient (MFCC) and Perceptual Linear Predictive (PLP) are the most common techniques used for feature extraction. In the systematic literature review presented by [PERSON] et al. [20], for instance, 69.5% of the retained papers used the MFCC technique to extract features from speech. A wide range of techniques can also be used for classification. Examples of these techniques are Artificial Neutral Network (ANN), Hidden Markov Model (HMM), and Dynamic Time Warping (DTW).  
The development of Arabic speech recognition systems has increased in the past decades thanks to the availability of different open sources and toolkits for building and assessing ASR. These toolkits are Hidden Markov Model Toolkit (HTK), Carnegie Mellon University (CMU) Sphinx engine, and KALDI Speech Recognition Toolkit. Speech recognition can be subdivided into four types [15], namely:  
* Isolated word recognition in which speakers pause momentarily between every spoken word;
* Continuous speech recognition allows speakers to speak almost naturally, with little or no breaks between words. The systems related to the second type are more complex than isolated word recognition and need large volumes of data to achieve excellent recognition rates;
* Connected words allow a minimal pause between the isolated utterances to be used together;
* Spontaneous speech remains normal-sounding and not conversational speech.  
Each category of speech recognition can be further categorized according to two sub-categories, namely:  
Figure 1: Automatic speech recognition architecture.  
1. Speaker-dependent, in which the system is based only on the speech of a specific speaker for which the system is trained [21];
2. Speaker-independent means speech recognition systems can be found in any speaker's speech [21].  
As presented earlier, this paper aims to review and analyze the existing studies on automatic speech recognition for the Arabic language. Seven fundamental research questions are tackled to provide insight, for instance, into the used toolkits for Arabic ASR and the applied feature extraction and classification techniques. The following section presents these questions and details the adopted search method in this SLR.