Speech Emotion Recognition Using Energies in six bands and Multilayer Perceptron on RAVDESS Dataset

Emotional information has recently received in-creased attention in studies on speech signals. Selecting the ideal speech feature representation for speech emotion recognition (SER) is the most difficult task. According to the statistical study, the functions of each speech characteristic vary in various emotional states, suggesting that various features have varying capacities for emotion distinction. A feature extraction and classification technique to create a reliable recognition system is presented in this article. We also provide new features that are based on energy and energy distribution taken from the (RAVDESS) database. Based on the results of the experiments, it can be concluded that the suggested strategy, when compared to a direct (multi-class classification).