# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 4 Experiment Results and Discussion
### Dataset Collection Procedure  
To build a natural and representative speech dataset, a rigorous methodology was designed. Subsequently, we identified the essential contributor characteristics and optimal recording environment for our dataset. Many civilizations, languages, religions, and ancient cultures have crossed into Morocco, such as Phoenician, Latin, Jewish, Christian, Islamic, Amazigh, and Arabic, as well as French, Spanish, and Portuguese [47]. While some of these cultures have diminished over time, their traces have merged with Moroccan culture and blended with it to form its unique identity. This is evident in many civilizational aspects, such as linguistic interaction between Arabic and Amazigh, alongside the Hassani oral culture, which blends the culture of Arab Moroccans with that of Mauritania. Additionally, the presence of foreign languages like French, Spanish, and English further enriches Morocco, making it a country rich in dialects where each region is characterized by its dialect and lexicon. Despite homogenization, the original dialect remains apparent through pronunciation, intonation, and letter articulation [48; 49]. As shown in Figure 5, a linguistic study conducted by [PERSON] [50] aimed to categorize the various Moroccan dialects, revealing that there are seven main dialects:  
1. Northern dialect/Jebli. 2. Northeastern dialect/Rif Amazigh language/Tarifite. 3. Eastern Moroccan dialect/Oriental Moroccan Arabic/Bedwi. 4. Nord-Ouest dialect/Standard Moroccan Arabic dialect/Rubi. 5. languages of the Middle Atlas/Tamazight. 6. languages of the Souss/Tachelhite. 7. Sahara Arabic dialects/Hassani Arabic/Ribi. To construct a representative dataset that captures the variations in speech patterns, accents, intonation, and dialects among individuals, we decided to select four contributors from each distinct region representing the seven dialects (as shown in Figure 6). As a result, the set of participants consists of 28 contributors, with an equal distribution of 14 males and 14 females. The age range within the group is varied, with eight individuals under the age of 25, 14 between 25 and 40, and six over 40. In addition to the seven dialect categorizations, we discovered that variations exist in the way certain sounds or letters are pronounced or used within the same dialect category. For instance, in some areas, the sound represented by the letter \"k\" might be replaced by the sound represented by \"g\", or the sound represented by \"kh\" might be replaced by \"h\". Subsequently, to collect and include all dialect differences, we would require considering over 100 distinct variations, which is beyond the scope of our research. Therefore, we opted for a standardized language that is comprehensible to all Moroccan Arabic speakers. However, we did consider some significant dialectal differences, particularly those where entire words are changed rather than just a single letter or pronunciations, ensuring that the dataset reflects meaningful linguistic distinctions without becoming overly complex. For example, the command \"lock the door\" is articulated differently across dialects. In the Hassani dialect, it is \"->  
dialect, it is \"\(\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\)--kayn al-haman\", and in other dialects, it is \"\(\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\)--kayn al-harara\". Regarding the recording environment, to ensure the representativity of our datasets, half contributors were tasked with recording commands in a controlled, clean environment, and the other half in a moving car (on the passenger side). This strategy introduces variability by simulating real-world scenarios where commands might be issued under different conditions. By capturing data in both static and dynamic environments, we aim to create a comprehensive dataset that reflects the diverse circumstances in which these commands are likely to be used.