Enhancing Human Emotion Classification in Human-Robot Interaction

Speech Emotion Recognition (SER) is vital for enhancing Human-Robot Interaction (HRI). In the past decade, various models have been applied to diverse datasets. However, these datasets often fell short in representing real-world scenarios. This paper introduces an innovative acoustic feature set, based on Mel Frequency Cepstral Coefficients (MFCC) and Mel-Spectrograms, applied to a combined dataset comprising RAVDESS, TESS, and EmoDB. Our proposed model achieves an impressive accuracy of 85.05 % on this combined dataset, demonstrating its suitability for real-world applications. This advancement holds significant promise for improving HRI systems, affective computing, and AI -driven applications, where understanding and responding to human emotions through speech is crucial. The robustness and high accuracy of our model provide valuable insights for re-searchers and practitioners seeking to implement SER technology in practical, real-world settings. This study offers two primary contributions. Firstly, it involves the compilation of a unified dataset from the three mentioned sources. Secondly, it achieved the highest accuracy of 85.05 %.