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Speech emotion recognition (SER) as a Machine Learning (ML) problem continues to garner a significant amount of research interest, especially in the affective computing domain. This is due to its increasing potential, algorithmic advancements, and applications in real-world scenarios. Human speech contains para-linguistic information that can be represented using quantitative features such as pitch, intensity, and Mel-Frequency Cepstral Coefficients (MFCC). SER is commonly achieved following three key steps: data processing, feature selection/extraction, and classification based on the underlying emotional features. The nature of these steps, coupled with the distinct features of human speech, underpin the use of ML methods for SER implementation. Recent research works in affective computing employed various ML methods for SER tasks; however, only a few of them capture the underlying techniques and …