Abstract:

Almost all instructors in the education domain face the challenge of understanding how engaged the participants or students are with the class ses-sions. Understanding the engagement levels of students is vital to grasp early warnings for any potential dropouts. This study proposes using Speech Emotion Recognition to understand the emotions underneath the verbal feedback given by students for the conducted class sessions, thereby enabling instructors to grasp student engagement.

Instead of focusing on the semantic contents of the speech, Speech Emotion Recognition focuses on the emotion contained in the speech signal. This work describes the development stages of a Speech Emotion Recognition Model to deduce the student’s emotional state while giving verbal feedback on conducted class sessions. This study considered various approaches based on Machine Learning classification techniques, such as Support Vector Machines, Random Forest, Decision Tree, Extreme Gradient Boosting, and Deep Learning classifi-cation techniques based on Long Short-Term Memory. Various features from the speech signals were extracted and narrowed those down to features of im-portance for the classification task.The developed Speech Emotion Recognition Models are trained and tested on the Ryerson Audio-Visual Database of Emotional Speech and Song and the Toronto Emotional Speech Set. The respective accuracy values of the devel-oped Models are 84.27 % for the Machine Learning model and 84 % for the Deep Neural Network model. Instructors can use the developed system to as-sess student engagement levels to get early warning signals of potential drop-outs.

Keywords:

Speech Emotion Recognition, Student Feedback, Student Dropout, Student Engagement Level, Long Short-Term Memory, Education Domain, Deep Neural Network, Random Forest, Deep Learning, Machine Learning.

Conference Published in: 7^th International Joint Conference on Advances in Computational Intelligence IJCACI 2023