# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 2 Literature Review  
Speech recognition systems have existed for a long time. They have been applied across various sectors and proven to be highly functional, contributing to the development of robust systems in many industries [7]. These significant advancements have primarily been in English. However, recently there has been a growing interest directed towards the Arabic language.  
[PERSON] et al. [8] constructed an Arabic dataset comprising 40 commands derived from the Google Speech Command (GSC) dataset [9] The constructed dataset is not tailored to a specific domain; rather, it comprises general commands. To benchmark the dataset, authors employed MFCC for feature extraction along with CNN for classification, by which they achieved an accuracy of 97.97%.  
[PERSON] and [PERSON] (2022) [10] developed an Egyptian Arabic dialect SR system to control a mobile assistant robot by recognizing five commands (stop, left, right, forward, backward). They compared various feature-extraction techniques and machine-learning (ML) models. The best result was achieved by training the SVM classifier with a combination of MFCC, spectral centroid, and signal power coefficient, achieving an accuracy of 95.48%.  
[PERSON] et al. (2009) [11], developed an Arabic system that detects spotted words in a continuous speech to control a manipulator arm (i.e., robot arm TR45). To reduce the additive noise, authors employed the Kalman filter introduced in [12]. For feature extraction, MFCC, MFCC delta, MFCC double delta, and energy coefficients were utilized, and the Hidden Markov Model was used for command classification. The authors improved the recognition ratio by employing high-quality microphones and combining HMM with Gaussian mixture models [13].  
[PERSON] and [PERSON] (2016) [14] constructed an Arabic SR system to control the motion of an intelligent automated mobile robot via voice commands by recognizing five commands (stop, left, right, forward, backward). Features are extracted using MFCC, and the Dynamic Time Warping technique was used to classify the commands. As a result, they achieved an accuracy rate of 87%.  
While there have been commendable contributions to the field of Arabic speech recognition, there remains a noticeable gap, particularly due to the linguistic diversity within the Arabic language. The development of robust speech recognition systems is challenging because Arabic is not a monolithic language; it consists of numerous dialects that vary significantly across regions. Consequently, more research is necessary to bridge this gap and create systems that can effectively cater to the wide range of dialects spoken by Arabic speakers.  
Furthermore, much of the existing research has been predominantly focused on Modern Standard Arabic (MSA). Although MSA is the formal version of the language, it is not commonly spoken in daily life. The reality is that the vast majority of Arabic speakers communicate in their regional dialects, which differ considerably from MSA. This creates a discrepancy between the speech recognition models being developed and the actual linguistic needs of the users.  
Even within the research that addresses dialectal Arabic, many systems are limited to recognizing basic commands, such as \"left\", \"right\", \"start\", and \"stop\". While these efforts are respectable, they do not fully address the complexities and nuances of dialectal Arabic in more specialized applications.  
Our work sets itself apart by focusing on the creation of a comprehensive dialectal Arabic speech command recognition dataset and system specifically tailored for the automotive field. This system is designed to handle the intricacies of dialectal Arabic, providing more accurate and reliable speech recognition in real-world, noisy environments where drivers may issue commands in their native dialects rather than MSA. This approach not only advances the field but also addresses a critical need to make speech recognition technology more accessible and functional for Arabic speakers across different regions.