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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/16709
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| タイトル: | Multimodal Feature Fusion for Human Personality Traits Classification |
| 著者: | Shen, Zhihao
Elibol, Armagan
Chong, Nak Young |
| 発行日: | 2020-06 |
| 出版者: | Korea Robotics Society |
| 誌名: | Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR) |
| 抄録: | Similar to human-human social interaction, the process of inferring a user’s personality traits during human-robot interaction plays an important role. Robots need to be endowed with such capability in order to attract user engagement more. In this study, we present our on-going research on obtaining variable-length multimodal features and their fusion to enable social robots to infer human personality traits during face-to-face human-robot interaction. Multimodal nonverbal features, including head motion, face direction, body motion, voice pitch, voice energy, and Mel-frequency Cepstral Coefficient (MFCC), were extracted from videos and audios recorded during the interaction. The different combinations of multimodal features were verified, and their classification performance was compared. |
| Rights: | Zhihao Shen, Armagan Elibol, Nak Young Chong, Multimodal Feature Fusion for Human Personality Traits Classification, Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR), Kyoto, Japan, June 22-26, Late Breaking Results Paper, 2020. This material is posted here with permission of Korea Robotics Society (KROS). |
| URI: | http://hdl.handle.net/10119/16709 |
| 資料タイプ: | author |
| 出現コレクション: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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このアイテムのファイル:
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記述 |
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| C4_UR20_0190_FI.pdf | | 102Kb | Adobe PDF | 見る/開く |
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