Lecturer Evaluation from the Perspective of Speech Emotion Recognition with Deep Learning

The subjective nature of lecturer evaluations, such as the Student Lecturer Evaluation, often fails to accurately reflect teaching quality due to external factors, including strict grading systems and student biases. To address this, this study proposes an objective evaluation framework using Speech Emotion Recognition (SER) technology, which analyzes lecturers’ emotions during teaching based on their voice characteristics. Leveraging deep learning methods, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN+LSTM, this research evaluates lecturer performance by extracting key voice features such as Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Mel, Energy, and Zero Crossing Rate (ZCR). A total of 54 experimental scenarios were conducted, varying the number of MFCC coefficients (13, 25, 40) and model architectures to optimize emotion classification. Results demonstrate that the CNN+LSTM hybrid model outperforms standalone CNN and LSTM models, achieving the highest accuracy (0.96) and lowest loss (0.14). The optimal number of MFCC coefficients was found to be 25, striking a balance between feature richness and computational efficiency. The implementation of this framework involves analyzing lecturer voice recordings, splitting them into small frames (2 ms each), and classifying emotions into positive, negative, or neutral categories. The frequency of each emotion is visualized in a bar chart, providing an objective assessment of the lecturer’s performance. This study highlights the potential of SER technology as an objective tool for lecturer evaluation and contributes to the advancement of voice-based emotion recognition in educational contexts.