Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 14, November 2019

Proposing A Hybrid Approach for Emotion Classification using Audio and Video Data


Reza Rafeh1, Rezvan Azimi Khojasteh2, Naji Alobaidi3, 1Waikato Institute of Technology, New Zealand, 2Islamic Azad University, Iran and 3Unitec Institute of Technology, New Zealand


Emotion recognition has been a research topic in the field of Human Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%.


Emotion Classification, Emotions Analysis, Emotion Detection, SVM, Speech Emotion Recognition