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The study is centered on the development of an advanced Speech Emotion Detection system utilizing Convolutional Neural Networks (CNN) and leveraging diverse datasets such as RAVDESS, CREMA-D, TESS, and SAVEE. Through rigorous analysis, the project achieved a notable accuracy rate of approximately 97% in identifying emotional cues within speech signals. The overarching goal of the research is to enrich emotional intelligence within technology, elevate interactions between humans and machines, and propel the domain of Speech Emotion Detection towards a more profound comprehension of human emotions conveyed through speech.
Bharath et al. (Wed,) studied this question.