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Jinlei Zhang1, Xue Qiu1, Xiang Li1

  • 1College of Computer and Information Science, Chongqing Normal University, Chongqing, China.

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Summary
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This study introduces a novel multimodal emotion recognition model using many-objective optimization. The approach enhances human-robot interaction by integrating voice and facial data for more natural communication.

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Emotion recognition is crucial for natural human-computer interaction.
  • Current systems often rely on single modalities, limiting performance.
  • Integrating multiple data sources can improve emotion recognition capabilities.

Purpose of the Study:

  • To propose a novel multimodal emotion recognition model.
  • To utilize a many-objective optimization algorithm for simultaneous optimization of accuracy and uniformity.
  • To enhance natural human-robot interaction through improved emotion sensing.

Main Methods:

  • Development of a multimodal model integrating voice and facial information.
  • Application of a many-objective optimization algorithm to the model.
  • Comparative analysis against single-modal models and the ISMS_ALA model.

Main Results:

  • The proposed multimodal model significantly outperforms single-modal approaches across all evaluation metrics.
  • Achieved a 2.88% higher accuracy compared to the ISMS_ALA model.
  • Demonstrated the effectiveness of many-objective optimization in improving multimodal emotion recognition.

Conclusions:

  • The many-objective optimization algorithm effectively enhances multimodal emotion recognition model performance.
  • The proposed model offers a significant advancement in sensing and expressing emotions for AI systems.
  • This research paves the way for more natural and intuitive human-robot interactions.