Speech Emotion Recognition using Dual-Conv2D architecture

The ability to convey emotions through speech is still of interest to the field of research. Where different neural network architectures have been developed to be able to automatically recognize emotions. In this work, the main objective is to develop an accurate neural network architecture for Speech emotion recognition. The work includes two main parts which essentially concern the use of MFCC as a feature extractor. And present a new technique for creating a CNN architecture based on the use of two separate architectures based on the Conv2D model.