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A multimodal emotion detection system during human-robot interaction.

Fernando Alonso-Martín1, María Malfaz, João Sequeira

  • 1Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid 28911, Spain. fernando.alonso@uc3m.es.

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Summary
This summary is machine-generated.

This study presents a multimodal system for social robots to detect user emotions using voice and facial analysis. The integrated system achieves high accuracy in emotion recognition, enhancing human-robot interaction.

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Social robots require sophisticated emotion detection for effective human-robot interaction.
  • Existing systems often rely on single modalities, limiting accuracy.

Purpose of the Study:

  • To develop and evaluate a multimodal system for user emotion detection in social robots.
  • To integrate voice and facial expression analysis for improved emotion recognition accuracy.

Main Methods:

  • Developed Gender and Emotion Voice Analysis (GEVA) using Chuck.
  • Developed Gender and Emotion Facial Analysis (GEFA) integrating SHORE and CERT.
  • Applied a decision rule to combine GEVA and GEFA outputs.
  • Integrated the system with the Robotics Dialog System (RDS) and ROS.

Main Results:

  • The multimodal system demonstrated a high success rate in user emotion recognition.
  • Combined audio-visual emotion detection significantly improved accuracy over individual modalities.
  • Experiments with real users validated the system's performance.

Conclusions:

  • The multimodal approach enhances emotion detection capabilities for social robots.
  • The developed system effectively integrates user emotion into the robot's dialog strategy.
  • The system shows promise for more natural and satisfying human-robot interactions.