Autonomous Speech Volume Control for Social Robots in a Noisy Environment Using Deep Reinforcement Learning

This paper presents a novel approach to automatically adjusting the speech volume of a socially assistive humanoid robot to enhance the quality of human-robot interactions. We apply the Deep Q-learning algorithm to enable the robot to adapt to the preferences of a user in the volume of the robot...

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Vydáno v:2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) s. 1263 - 1268
Hlavní autoři: Bui, Ha-Duong, Chong, Nak Young
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2019
Témata:
Biomimetics
Conferences
Control systems
Human-robot interaction
Humanoid robots
Q-learning
Reinforcement Learning
Social Human-Robot Interaction
Speech enhancement
Speech Volume Control
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Shrnutí:This paper presents a novel approach to automatically adjusting the speech volume of a socially assistive humanoid robot to enhance the quality of human-robot interactions. We apply the Deep Q-learning algorithm to enable the robot to adapt to the preferences of a user in the volume of the robot's voice in social contexts. Subjective experiments were conducted to verify the validity of the proposed system. Twenty-three human subjects had social conversations with humanoid robots across various noisy environments. Participants rated their perception of the robots' voices in terms of clearness and comfortability through a questionnaire. The results show that the robot equipped with our framework outperforms other experimental robots in trials. This study confirmed the effectiveness of the proposed autonomous speech volume control system for social robots communicating with people in noisy environments.
DOI:10.1109/ROBIO49542.2019.8961810