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Enhancement of the interlocutor emotion recognition rate from non-professionals speakers in Arabic database

This work is a contribution to the enhancement of the emotional regardless of speaker recognition rate for the Arabic language, which is a poorly endowed language in this field. This problem is still relevant, especially for databases created by non-professional speakers(the emotion produced by professionals is standard whereas that produced by naive people is more like a spontaneous emotion because the emotion depends very much on the speaker). This is why a comparative study between two databases: one in Modern Standards Arabic (MSA) which was carried out by non-professionals recorders (students) and the other in German simulated by actors. The study was carried out using statistical methods (K nearest neighbors, Support Vector Machine, Extratress, Random Forest, Gradient Boosting) as well as a Neural Network approach which are the Convolutional Neural Network and Multi-Layer Perceptron, to identify four emotions (Anger, Happiness, Neutral and Sadness). The latter showed a clear improvement in terms of accuracy from 47.4% to 64% for MSA.