Proposing a hybrid approach for emotion classification using audio and video data

Rafeh, Reza and Azimi Khojasteh, Rezvan and Naji, Alobaidi (2019) Proposing a hybrid approach for emotion classification using audio and video data. 5th International Conference on Computer Science and Information Technology (CSTY 2019), Dubai, UAE, 30 November - 1 December 2019.

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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%.

Item Type:Paper presented at a conference, workshop or other event, and published in the proceedings
Keywords that describe the item:Emotion Classification, Emotions Analysis, Emotion Detection, SVM, Speech Emotion Recognition;
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
ID Code:7276
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Deposited On:04 May 2020 03:50
Last Modified:04 May 2020 03:50

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