Title: Ideological and political empowering English teaching: ideological education based on artificial intelligence in classroom emotion recognition

Authors: Liqun Zhang

Addresses: Handan Polytechnic College, Handan, Hebei, China

Abstract: Emotions play an important role in human decision-making, interaction and cognition. Accurately identifying human emotions can provide effective support for human decision-making and solutions. Reflecting the learning situation with classroom emotions can assist teachers in implementing teaching interventions, so as to help teachers carry out accurate teaching. In this paper, we propose a classroom emotion recognition method based on multi-modal fusion of speech and text. We first adopt the CNN and LSTM to extract the spatio-temporal feature from the speech data. Then, we adopt a LSTM model to perform feature extraction on text data. After obtaining these two types of features, we finally design a fusion model based on attention mechanism. The experimental results prove that the method proposed in this paper has good prediction performance, which is of great significance for the classroom emotion recognition.

Keywords: emotion classification; multi-modal feature fusion; neural network; feature extraction.

DOI: 10.1504/IJCAT.2023.132103

International Journal of Computer Applications in Technology, 2023 Vol.71 No.3, pp.265 - 271

Received: 30 Apr 2022
Received in revised form: 21 Jun 2022
Accepted: 24 Jun 2022

Published online: 11 Jul 2023 *

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