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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.

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Decoding Digital Emotions: Advancing Online Learning with Speech-Emotion Recognition Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Sherif Welsen,
  • Yiyang Liu

摘要

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.