## 2 Related Work  
In recent years, neural network-based modeling approaches, including variants such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures, have made notable strides in the realm of speech emotion recognition. This has led to a significant enhancement in the capacity to accurately detect and classify emotions from spoken language. One of the key advantages of deep learning models is their ability to automatically learn hierarchical representations of data. RNNs are particularly good at capturing temporal relationships in sequential data; like speech, they have been utilized extensively for speech emotion recognition. RNNs can represent the dynamics and context of emotional expressions by processing speech signals across time. This allows for more precise emotion recognition. CNNs, however, are good at picking up spectral characteristics and local patterns in voice signals. They can examine the spectral content of brief speech segments, which is essential for differentiating between emotional states. Furthermore, advancements in deep learning architectures, such as long short-term memory (LSTM) [12] networks, gated recurrent units (GRUs) [13], and attention mechanisms [14], have further improved the performance of deep learning models for speech emotion recognition. While SER has seen significant progress in languages like English, Spanish, and German, the research focus on Arabic speech emotion recognition is relatively limited. This is notable given Arabic's status as one of the most widely spoken languages globally [15]. Recent studies have attempted to fill this gap by developing models and datasets for Arabic SER. For instance, [PERSON] et al. [16] explored the application of CNN models for Arabic speech emotion recognition using the KSUEmotions corpus. The authors demonstrated that CNN models outperformed traditional machine learning approaches, effectively capturing local features in speech signals. Furthermore, the study also compared the performance of CNNs with other architectures like ResNet (Residual Network) and CRNN (convolutional recurrent neural network), highlighting CNNs' advantages in multilingual emotion recognition tasks. Thiscontribution enriches the understanding of CNNs' potential for SER, particularly in the context of Arabic language datasets. In [17], the authors proposed two models for emotion recognition using the KSUemotions dataset. The first approach integrates an attention mechanism with a convolutional neural network (CNN), while the second employs a deep CNN. The attention-based model achieved a 2.2% improvement in performance over the CNN baseline, whereas the deep CNN demonstrated faster training and classification times. The aforementioned studies have established significant foundations; nevertheless, challenges persist due to the paucity of labeled Arabic datasets and the variability between Arabic dialects. To address these challenges, researchers such as [PERSON] et al. [18] and [PERSON] et al. [19] have presented datasets focusing on specific dialects, including Emirati Arabic and Egyptian. This work underscores the growing recognition of the significance of dialect variation in Arabic SER. One critical area for improvement in speech emotion recognition (SER) models, particularly those for languages like Arabic where data availability is limited, is the implementation of data augmentation techniques. The utilization of techniques such as noise injection, pitch shifting, time warping, and volume adjustments is a common practice with the objective of artificially increasing the diversity of training datasets. The rationale behind this approach is to enhance model robustness and generalization. Additionally, augmentation helps mitigate class imbalance issues by generating synthetic variations of underrepresented emotional categories, improving classification performance.