## 1 Introduction  
In recent years, speech emotion recognition (SER) has garnered significant attention due to its applications in human-computer interaction [1], psychological assessment [2], and entertainment technologies such as robotics [3] and computer games [4]. The ability of machines to detect and interpret human emotions from speech enhances user experience, enabling more empathetic and natural interactions in various domains, such as virtual assistants and automated customer service. The advent of machine learning and deep learning approaches has led to notable advances in SER systems. However, one of the critical challenges in this area is the limited availability of large, balanced datasets that capture a wide range of emotional expressions in speech [5].  
This challenge is particularly pronounced in Arabic speech emotion recognition, where the complexity of dialects and regional variations poses significant difficulties for effective emotion recognition. Arabic, one of the most widely spoken languages globally, includes distinct dialects that differ in pronunciation, intonation, and emotional expression [6]. Despite its importance, Arabic SER has received less attention compared to other languages such as English, German, and French [7]. This gap is compounded by the scarcity of labeled emotional datasets for Arabic speech, limiting the performance of machine learning models in this area.  
A promising solution to address the problem of small and imbalanced datasets is data augmentation, which involves artificially increasing the size and variability of training data. Techniques such as noise addition, volume adjustments, and time shifting are commonly used in speech processing to simulate real-world variations, improving model robustness and generalization [8; 9]. Although these techniques have been applied effectively in other languages, their impact on Arabic speech emotion recognition remains largely unexplored.  
In this study, we aim to address the challenges of limited data in Arabic SER by investigating the effect of multi-technique data augmentation on the performance of convolutional neural networks (CNNs). Instead of proposing a novel SER architecture, we focus on enhancing the training process using augmentation techniques to improve the model's ability to generalize across different dialects and emotional expressions. Specifically, we employ two datasets: the Saudi Dialect dataset [10], which captures regional variations in Arabic, and the BAVED (Basic Arabic Vocal Emotions Dataset), which represents standard Arabic emotions [11]. By applying various augmentation techniques, we aim to evaluate their effectiveness in improving emotion recognition accuracy and determine the optimal number of augmentations to enhance model performance.  
This paper is structured as follows: Section 2 presents a review of related work in SER, with a particular focus on Arabic speech emotion recognition and data augmentation techniques. Section 3 then provides an explanation of the proposed approach and system architecture. Section 4 presents and discusses the results, while Section 5 concludes with a summary of the key findings and an outline of future perspectives.