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タイトル: アンドロイドERICAの傾聴対話システム --人間による傾聴との比較評価--
その他のタイトル: An Attentive Listening System for Autonomous Android ERICA: Comparative Evaluation with Human Attentive Listeners
著者: 井上, 昂治  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-2929-2559 (unconfirmed)
ラーラー, ディベッシュ  KAKEN_name
山本, 賢太  KAKEN_name
中村, 静  KAKEN_name
高梨, 克也  KAKEN_name
河原, 達也  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-2686-2296 (unconfirmed)
著者名の別形: Inoue, Koji
Lala, Divesh
Yamamoto, Kenta
Nakamura, Shizuka
Takanashi, Katsuya
Kawahara, Tatsuya
キーワード: attentive listening
spoken dialogue system
autonomous android
backchannel
listener response
発行日: 1-Sep-2021
出版者: 人工知能学会
誌名: 人工知能学会論文誌
巻: 36
号: 5
論文番号: H-L51
抄録: An attentive listening system for autonomous android ERICA is presented. Our goal is to realize a humanlike natural attentive listener for elderly people. The proposed system generates listener responses: backchannels, repeats, elaborating questions, assessments, and generic responses. The system incorporates speech processing using a microphone array and real-time dialogue processing including continuous backchannel prediction and turn-taking prediction. In this study, we conducted a dialogue experiment with elderly people. The system was compared with a WOZ system where a human operator played the listener role behind the robot. As a result, the system showed comparable scores in basic skills of attentive listening, such as easy to talk, seriously listening, focused on the talk, and actively listening. It was also found that there is still a gap between the system and the human (WOZ) for high-level attentive listening skills such as dialogue understanding, showing interest, and empathy towards the user.
著作権等: © 人工知能学会2021
ここに掲載した著作物の利用に関する注意 本著作物の著作権は人工知能学会に帰属します。本著作物は著作権者である人工知能学会の許可のもとに掲載するものです。ご利用に当たっては「著作権法」に従うことをお願いいたします。
Notice for the use of this material. The copyright of this material is retained by the Japanese Society for Artificial Intelligence (JSAI). This material is published on this web site with the agreement of the author(s) and the JSAI. Please be complied with Copyright Law of Japan if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. All Rights Reserved, Copyright (C) The Japanese Society for Artificial Intelligence.
URI: http://hdl.handle.net/2433/269039
DOI(出版社版): 10.1527/tjsai.36-5_h-l51
出現コレクション:学術雑誌掲載論文等

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