# Efficient and Robust Arabic Automotive Speech Command Recognition System
###### Abstract  
The automotive speech recognition field has become an active research topic as it enables drivers to activate various in-car functionalities without being distracted. However, research in Arabic remains nascent compared to English, French, and German. Therefore, this paper presents a Moroccan Arabic automotive speech recognition system. Our system aims to enhance the driving experience to make it comfortable and safe while assisting individuals with disabilities. We created a speech dataset comprising 20 commonly used car commands. It consists of 5600 instances collected from Moroccan contributors and recorded in clean and noisy environments to increase its representativity. We used MFCC, weighted MFCC, and Spectral Subband Centroids (SSC) for feature extraction, as they demonstrated promising results in noisy settings. For classifier construction, we proposed a hybrid architecture, consisting of Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN). Training our proposed model with WMFCC and SSC features achieved an accuracy of 98.48%, outperforming all baseline models we trained and outperforming the existing solutions in the state-of-the-art literature. Moreover, it shows promising results in a clean and noisy environment and maintains resilience to additive Gaussian noise while using few computational resources.  
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Accordingly, almost all new cars are equipped with a voice command control option. This advancement was primarily in English, French, and German languages. However, research in this field for the Arabic language's different dialects, including the classical one, is still in its early stages [5].  
The Arabic language is the fifth-most widely spoken language globally, with 453 million speakers, and it is considered the official language of 22 countries [6]. It is classified into three types: Classic Arabic (CA), which is the language of the Quran, Modern Standard Arabic (MSA), which is used in professional areas, and Dialectal Arabic, a non-professional form used in daily life communication. These dialects depend on regional geography, and they differ significantly from one another in vocabulary and pronunciation. Commonly, there are five Arabic dialects: Levantine, Iraqi, Gulf, Egyptian, and Maghrebi Arabic.  
Considering the impact of the automotive speech recognition field in people's lives, in this paper, we present our efforts to construct an Arabic Speech Command Recognition system for automobiles based on deep-learning architectures. To achieve this, we have focused on building a Maghrebi dialect dataset, more specifically the Moroccan dialect, and an end-to-end hybrid deep-learning architecture utilizing a combination of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BI-LSTM) model.  
To the best of our knowledge, this is the first project dedicated to automotive speech recognition of the Arabic language in the Arab world. Therefore, the main contributions of this paper are summarized as follows:  
* We constructed the first automotive speech recognition system in the Arabic language in the Arab world.
* We built the first Moroccan Arabic dataset in the automotive speech recognition field that is representative and simulates real-time conditions.
* We developed a hybrid deep-learning model that combines Bidirectional Long Short-Term Memory architecture and a Convolutional Neural Network into one unified architecture Bi-LSTM-CNN for driver command reorganization in the Arabic language.
* We developed a model that outperformed the proposed method in the state-of-the-art literature.  
This paper is organized as follows. Section 2 presents related works. In Section 3, the methodology of building the dataset and the model is described. Section 4 presents and discusses the results of the experiments. Finally, the conclusion of the paper and the scope of future work are highlighted in Section 5.