TY - CHAP
T1 - Decoding Digital Emotions
T2 - Advancing Online Learning with Speech-Emotion Recognition Systems
AU - Welsen, Sherif
AU - Liu, Yiyang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This chapter introduces a novel system tailored for emotion recognition in speech within online educational platforms. Developed using MATLAB, this system harnesses cutting-edge machine learning methodologies, employing datasets from the Berlin Database of Emotional Speech (EmoDB) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Precise detection and categorization of emotional expressions in speech are made possible by the hybrid model it employs, which combines Long Short-Term Memory (LSTM) networks with one- and two-dimensional convolutional neural networks. This system effectively improves the interpretation of student emotions in virtual learning contexts, achieving an impressive accuracy rate of 83.95%. However, it is important to note that this work also underscores the necessity for ongoing research to further refine the system's performance and dependability. This endeavour marks a crucial advancement in customizing online education, aiming to foster more empathetic and engaging virtual learning environments.
AB - This chapter introduces a novel system tailored for emotion recognition in speech within online educational platforms. Developed using MATLAB, this system harnesses cutting-edge machine learning methodologies, employing datasets from the Berlin Database of Emotional Speech (EmoDB) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Precise detection and categorization of emotional expressions in speech are made possible by the hybrid model it employs, which combines Long Short-Term Memory (LSTM) networks with one- and two-dimensional convolutional neural networks. This system effectively improves the interpretation of student emotions in virtual learning contexts, achieving an impressive accuracy rate of 83.95%. However, it is important to note that this work also underscores the necessity for ongoing research to further refine the system's performance and dependability. This endeavour marks a crucial advancement in customizing online education, aiming to foster more empathetic and engaging virtual learning environments.
KW - Convolution Neural Networks
KW - Intelligent Tutoring
KW - Long Short-Term Memory
KW - Smart Campus
KW - Speech Emotion Recognition (SER)
UR - http://www.scopus.com/inward/record.url?scp=105002768333&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-4952-5_10
DO - 10.1007/978-981-96-4952-5_10
M3 - Book Chapter
AN - SCOPUS:105002768333
T3 - Lecture Notes in Educational Technology
SP - 139
EP - 151
BT - Lecture Notes in Educational Technology
PB - Springer Science and Business Media Deutschland GmbH
ER -