Speech Emotion Recognition System for Arabic Speakers

The Speech Emotion Recognition (SER) system is one of the essential human-computer interface applications. Despite the rapid advancement of technology, there is still a gap in SER research in the Arabic language corpus. The goal of this research is to build an Arabic-based SER based on a feature set that has both high performance and low computational cost. Two novel feature sets were implemented using a mix of spectral and prosodic features. An Arabic semi-natural corpus 'EYASE' was adopted for testing the proposed system. Five machine learning classifiers using the different feature sets were implemented. Featureset-2 showed promising results in association with the SVM classifier. To validate the results, a survey analysis was implemented on the Arabic-Egyptian corpus 'EYASE' used. © 2022 IEEE.