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Predicting and Synchronising Co-Speech Gestures for Enhancing Human-Robot Interactions Using Deep Learning Models.

Enrique Fernández-Rodicio1, Christian Dondrup2, Javier Sevilla-Salcedo1

  • 1Department of Systems Engineering and Automation, University Carlos III of Madrid, Av. de la Universidad, 30, 28911 Leganés, Spain.

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Summary
This summary is machine-generated.

This study presents a novel system for robots to generate synchronized speech and gestures, enhancing human-robot interaction. The deep learning model effectively predicts and aligns non-verbal cues with spoken language for more natural communication.

Keywords:
co-speech gesturesdeep learninggesture predictionhuman–robot interactiontransformer models

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Robots increasingly perform tasks requiring human interaction, necessitating their perception as suitable partners.
  • Animate robot appearance, achieved through expressions and gestures, is crucial for user acceptance.
  • Synchronizing robot speech and gestures presents a significant challenge for natural communication.

Purpose of the Study:

  • To develop a system that predicts and synchronizes robot gestures with its speech.
  • To enable robots to generate co-speech gestures that support verbal communication.
  • To improve human-robot interaction through enhanced non-verbal expressiveness.

Main Methods:

  • A deep learning-based prediction model was employed to label robot speech with appropriate expression types.
  • A rule-based synchronization module was developed to align predicted gestures with specific speech segments.
  • Two distinct approaches were evaluated: recurrent neural networks with conditional random fields, and transformer models.

Main Results:

  • The developed system successfully predicts and synchronizes gestures with robot speech.
  • The system demonstrates the ability to select appropriate co-speech gestures within real-time interaction constraints.
  • Both tested deep learning architectures proved effective in gesture prediction and synchronization.

Conclusions:

  • The proposed system enhances robot expressiveness by generating synchronized speech and gestures.
  • This advancement contributes to more natural and effective human-robot communication.
  • The research validates the use of deep learning for creating robots with improved social interaction capabilities.