TY - GEN
T1 - Inferring Human Personality Traits in Human-Robot Social Interaction
AU - Shen, Zhihao
AU - Elibol, Armagan
AU - Chong, Nak Young
N1 - Funding Information:
*This work was supported by the EU-Japan coordinated R&D project on “Culture Aware Robots and Environmental Sensor Systems for Elderly Support,” commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 Research and Innovation Programme under grant agreement No. 737858.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/22
Y1 - 2019/3/22
N2 - In this report, a new framework is proposed for inferring the user's personality traits based on their habitual behaviors during face-to-face human-robot interactions, aiming to improve the quality of human-robot interactions. The proposed framework enables the robot to extract the person's visual features such as gaze, head and body motion, and vocal features such as pitch, energy, and Mel-Frequency Cepstral Coefficient (MFCC) during the conversation that is lead by Robot posing a series of questions to each participant. The participants are expected to answer each of the questions with their habitual behaviors. Each participant's personality traits can be assessed with a questionnaire. Then, all data will be used to train the regression or classification model for inferring the user's personality traits.
AB - In this report, a new framework is proposed for inferring the user's personality traits based on their habitual behaviors during face-to-face human-robot interactions, aiming to improve the quality of human-robot interactions. The proposed framework enables the robot to extract the person's visual features such as gaze, head and body motion, and vocal features such as pitch, energy, and Mel-Frequency Cepstral Coefficient (MFCC) during the conversation that is lead by Robot posing a series of questions to each participant. The participants are expected to answer each of the questions with their habitual behaviors. Each participant's personality traits can be assessed with a questionnaire. Then, all data will be used to train the regression or classification model for inferring the user's personality traits.
KW - classification model
KW - human-robot interaction
KW - regression model
KW - social cue
KW - user personality traits
UR - http://www.scopus.com/inward/record.url?scp=85063995866&partnerID=8YFLogxK
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U2 - 10.1109/HRI.2019.8673124
DO - 10.1109/HRI.2019.8673124
M3 - Conference contribution
AN - SCOPUS:85063995866
T3 - ACM/IEEE International Conference on Human-Robot Interaction
SP - 578
EP - 579
BT - HRI 2019 - 14th ACM/IEEE International Conference on Human-Robot Interaction
PB - IEEE Computer Society
T2 - 14th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2019
Y2 - 11 March 2019 through 14 March 2019
ER -