Fatma Harby1, Mansor Alohali2, Adel Thaljaoui2,3,*, Amira Samy Talaat4
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2689-2719, 2024, DOI:10.32604/cmc.2024.046623
Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture the periodic nature of audio… More >
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