## 1 Introduction  
Speech emotion recognition (SER) is a research field aiming to develop systems that automatically recognize emotions from speech, with the potential to enhance user experiences in various applications like spoken dialogue systems, intelligent voice assistants, and computer games. However, the accuracy of current SER systems remains relatively low due to factors such as the intricate nature of human emotions, variability in human speech, and challenges in extracting reliable features from speech signals. A typical SER system comprises two main components: feature extraction and classification. Feature extraction is tasked with capturing essential acoustic characteristics from the speech signal, including pitch and spectral features. Subsequently, the classification component assigns emotional labels to the speech signal based on extracted features. In recent years, deep learning has emerged as a powerful tool for machine learning, finding success in domains like computer vision, speech recognition, and natural language processing. Deep learning is well suited for SER for several reasons. Firstly, deep learning models excel at capturing intricate relationships between features, crucial for accurately identifying emotions from speech. Secondly, deep learning models possess unique characteristics that enable them to efficiently leverage large speech datasets, a capability particularly advantageous in SER, where access to extensive annotated data facilitates model training and improves generalization to diverse speakers and emotional expressions. Additionally, while other models also claim the ability to generalize to new speakers and situations, deep learning models demonstrate a notable robustness and adaptability in handling variations, making them particularly effective in real-world SER applications. Here are some other points to keep in mind before going forward with the research:* The intricate nature of human emotions poses a challenge to developing accurate SER systems as emotions are complex and expressed in various ways. For instance, happiness may be conveyed through laughter, smiling, and a high-pitched voice, but also through tears, a from, and a low-pitched voice. * The variability in human speech further complicates SER systems, as individuals speak differently based on factors like age, gender, and accent. For example, a young woman from the United States may have a different speaking style than an older man from the United Kingdom. In the dynamic landscape of speech emotion recognition (SER), marked by its rapid growth, this research aims to contribute to the evolution of computer interaction by leveraging deep learning advancements to advance real-time emotion recognition from speech. The primary objective involves extracting pivotal features from audio waveforms to construct a model that accurately predicts emotions. While Mel-frequency cepstral coefficients (MFCC) feature extraction was employed in this study, it is important to acknowledge that other feature extraction methods exist and could be explored in future research to potentially enhance performance. MFCC is widely used and has shown success in many SER applications due to its ability to capture spectral characteristics relevant to speech perception, it is essential to note that there is not a universally \"best\" technique. Different techniques may perform better in different contexts. Four datasets (RAVDESS, SAVEE, TESS, and EmoDB) were utilized to train and evaluate the model. The model underwent initial training with linear and kernelized support vector machines (SVMs), followed by comprehensive training with a convolutional neural network (CNN) model incorporating long short-term memory (LSTM). Further refinement of the datasets was achieved through the application of a Gray Wolf Optimizer, tailoring the parameters for optimal model fitting. It is important to clarify that, while the study contributed to refining the models and optimization techniques, the datasets themselves were not crafted by the authors.