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A Classroom Emotion Recognition Model Based on a Convolutional Neural Network Speech Emotion Algorithm.

¹Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.

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|August 1, 2022

Summary

This study introduces a novel convolutional neural network (CNN) and recurrent neural network (RNN) model for speech emotion recognition in classrooms. The advanced model effectively identifies teacher emotions, enhancing educational insights.

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Area of Science:

Artificial Intelligence
Educational Technology
Speech Processing

Background:

Understanding teacher emotions is crucial for effective classroom management and student engagement.
Existing speech emotion recognition models face challenges with variable-length speech and feature extraction.

Purpose of the Study:

To develop an advanced speech emotion recognition model for analyzing teachers' classroom emotions.
To investigate the patterns and factors influencing teachers' emotional control in educational settings.

Main Methods:

A hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model was developed.
An attention mechanism was incorporated for variable-length speech emotion recognition.
Spatiotemporal and convolutional channel attention modules were designed to enhance feature extraction.

Main Results:

The proposed CNN-RNN model demonstrated high accuracy in speech emotion recognition.
Attention mechanisms effectively improved the model's ability to focus on relevant emotional features.
The model successfully identified characteristics and rules of teachers' emotional control.

Conclusions:

The developed speech emotion recognition model offers a powerful tool for analyzing classroom dynamics.
This research provides valuable insights into teachers' emotional control strategies.
The findings can inform pedagogical practices and teacher training programs.