###### Abstract  
In recent years data science has been applied in a variety of real-life applications such as human-computer interaction applications, computer gaming, mobile services, and emotion evaluation. Among the wide range of applications, speech emotion recognition (SER) is also an emerging and challenging research topic. For SER, recent studies used handcrafted features that provide the best results but failed to provide accuracy while applied in complex scenarios. Later, deep learning techniques were used for SER that automatically detect features from speech signals. Deep learning-based SER techniques overcome the issues of accuracy, yet there are still significant gaps in the reported methods. Studies using lightweight CNN failed to learn optimal features from composite acoustic signals. This study proposed a novel SER model to overcome the limitations mentioned earlier in this study. We focused on Arabic vocal emotions in particular because they received relatively little attention in research. The proposed model performs data augmentation before feature extraction. The 273 derived features were fed as input to the transformer model for emotion recognition. This model is applied to four datasets named BAVED, EMO-DB, SAVEE, and EMOVO. The experimental findings demonstrated the robust performance of the proposed model compared to existing techniques. The proposed SER model achieved 95.2%, 93.4%, 85.1%, and 91.7% accuracy on BAVED, EMO-DB, SAVEE, and EMOVO datasets respectively. The highest accuracy was obtained using BAVED dataset, indicating that the proposed model is well suited to Arabic vocal emotions. multilingual; transformer; SER; speech emotion recognition; Arabic vocal emotion; artificial intelligence +
Footnote †: journal: Medical Sciences  
Researchers have recently paid a lot of attention to the creation of an efficient human-technology communication interface, which falls under the category of human-computer interaction (HCI). In a broader perspective, emotion recognition from speech signals has been used in a variety of applications, including speech synthesis, robotics, automobile industry, multimedia technologies, healthcare, education, forensics, customer service, and entertainment [4]. The taxonomy of text-to-speech aids in the development of screen readers for the blind [5]. Nowadays, robotic technologies are increasingly evolving sensor data that comes from internal and external environmental factors, impacting decision-making through emotion recognition. Experimental paradigms that identify emotions from publicly available datasets must be carefully evaluated since they may fail when used in a real-world context [6]. In the automobile industry advanced image/video and voice-based signal processing methods are employed to identify drowsiness and lassitude in drivers [7; 8; 9]. The abstraction of hidden features of an individual or a system extracted from heterogeneous multimedia content for recommendation systems has received the attention of SER researchers [10]. Intensity detection of virus attacks through patients' voice signals has opened a new challenge area in medical science since the current COVID-19 spread [11]. Speech recognition techniques have recently been incorporated into online learning platforms for emotional analysis. Positive emotions consequently influence students' inspiration, dedication, and self-learning abilities. On the other hand, negative emotions leave an adverse impact on students' performance [12]. A recent study has discovered several significant facts in human brain images, demonstrating how they do cognitive-based speech synthesis, which has given rise to new research trends in automatic speech recognition (ASR) and forensic systems [11; 12; 13]. In order to provide high-quality customer care services, SER techniques are being applied to estimate the emotions of telemarketing agents and customers from their conversations. The desirable emotion detection can be achieved directly from speech signals.