This paper investigates the effectiveness of various data augmentation techniques for enhancing Arabic speech emotion recognition (SER) using convolutional neural networks (CNNs). Utilizing the Saudi Dialect and BAVED datasets, we address the challenges of limited and imbalanced data commonly found in Arabic SER. To improve model performance, we apply augmentation techniques such as noise addition, time shifting, increasing volume, and reducing volume. Additionally, we examine the optimal number of augmentations required to achieve the best results. Our experiments reveal that these augmentations significantly enhance the CNN’s ability to recognize emotions, with certain techniques proving more effective than others. Furthermore, the number of augmentations plays a critical role in balancing model accuracy. The Saudi Dialect dataset achieved its best results with two augmentations (increasing volume and decreasing volume), reaching an accuracy of 96.81%. Similarly, the BAVED dataset demonstrated optimal performance with a combination of three augmentations (noise addition, increasing volume, and reducing volume), achieving an accuracy of 92.60%. These findings indicate that carefully selected augmentation strategies can greatly improve the performance of CNN-based SER systems, particularly in the context of Arabic speech. This research underscores the importance of tailored augmentation techniques to enhance SER performance and sets a foundation for future advancements in this field.

This paper investigates the effectiveness of various data augmentation techniques for enhancing Arabic Speech Emotion Recognition (SER) using Convolutional Neural Networks (CNNs). Utilizing the Saudi Dialect and BAVED datasets, we address the challenges of limited and imbalanced data commonly found in Arabic SER. To improve model performance, we apply augmentation techniques such as noise addition, time shifting, increasing volume, and reducing volume. Additionally, we examine the optimal number of augmentations required to achieve the best results. Our experiments reveal that these augmentations significantly enhance the CNN's ability to recognize emotions, with certain techniques proving more effective than others. Furthermore, the number of augmentations plays a critical role in balancing model accuracy. The Saudi Dialect dataset achieved its best results with two augmentations (increasing volume and decreasing volume), reaching an accuracy of 96.81%. Similarly, the BAVED dataset demonstrated optimal performance with a combination of three augmentations (noise addition, increasing volume, and reducing volume), achieving an accuracy of 92.60%. These findings indicate that carefully selected augmentation strategies can greatly improve the performance of CNN-based SER systems, particularly in the context of Arabic speech. This research underscores the importance of tailored augmentation techniques to enhance SER performance and sets a foundation for future advancements in this field.