| A Comprehensive Survey of Speech Emotion Recognition Techniques: Databases, Features and Classification |
| Paper ID : 1118-ICEEM2023 (R1) |
| Authors: |
|
Neven Hassan *1, adel elfishawy2, fathi sayed1, Mohamed Samy Arafa3 1Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering, Menoufia University Menouf, Egypt 2Electronics and Electrical ‎Communications Engineering ‎Department Faculty of Electronic Engineering, ‎Menoufia University 3Department of Electronics and Electrical Engineering, Faculty of ELectronic Engineering, AlMenoufiya university,Egypt |
| Abstract: |
| The rapid advancements in technology, particularly in the field of artificial intelligence, have provided innovative and robust opportunities to explore various aspects of human emotional behavior. A crucial area of study in this regard is the cultural influence on the expression and perception of human emotions. Cultural groups often demonstrate an in-group advantage, making it easier for individuals within the same cultural group to accurately perceive each other's emotions. Emotion recognition from speech signals has been a subject of research for many years, particularly in human-machine interface applications. Numerous systems have been developed to identify emotions from speech signals. This paper presents a comprehensive survey of speech emotion classification, addressing three essential aspects in the design of a speech emotion recognition system. Firstly, it discusses the selection of suitable features for speech representation. Secondly, it explores the design of an appropriate classification scheme. Lastly, it emphasizes the importance of creating a well-prepared emotional speech database for evaluating the performance of the recognition system. |
| Keywords: |
| Speech emotion recognition, AI, Deep learning, classification, feature extraction, and a database of emotional speech. |
| Status : Paper Accepted |
