Estimating and Visualizing Persuasiveness of Participants in Group Discussions

• Item type: Journal(1)
• 公開日: 2023-02-15
• タイトル: Estimating and Visualizing Persuasiveness of Participants in Group Discussions
• タイトル / 言語: en
• タイトル: Estimating and Visualizing Persuasiveness of Participants in Group Discussions
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:インタラクションの理解および基盤・応用技術] datasets, neural networks, gaze detection, text tagging
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Seikei University
• 著者所属: Seikei University
• 著者所属: NTT Human Informatics Laboratories
• 著者所属: Seikei University
• 著者所属: NTT Human Informatics Laboratories
• 著者所属: NTT Human Informatics Laboratories
• 著者所属: NTT Human Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): Seikei University
• 著者所属(英): en
• 著者所属(英): Seikei University
• 著者所属(英): en
• 著者所属(英): NTT Human Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): Seikei University
• 著者所属(英): en
• 著者所属(英): NTT Human Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): NTT Human Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): NTT Human Informatics Laboratories
• 著者名: Atsushi, Ito; Yukiko, I. Nakano; Fumio, Nihei; Tatsuya, Sakato; Ryo, Ishii; Atsushi, Fukayama; Takao, Nakamura
• 著者名(英): Atsushi, Ito; Yukiko, I. Nakano; Fumio, Nihei; Tatsuya, Sakato; Ryo, Ishii; Atsushi, Fukayama; Takao, Nakamura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In group discussions, each participant has his/her own opinion, and if necessary, tries to persuade other participants to reach an agreement. Therefore, persuasiveness is an important skill for communicating with others. Based on this motivation, this study aims to estimate the persuasiveness of group discussion participants. First, human annotators rated the level of persuasiveness of each participant in group discussions among four people. We then created multimodal and multiparty models for estimating persuasiveness of a participant using speech, language, and visual (head pose) features using gated recurrent unit (GRU)-based neural network. In our experiment, in estimating the highest persuasive participant among the group, the performance of the proposed method was 76% in accuracy. In the binary classification task for estimating high or low persuasiveness participants among the group, the performance of the best performing multimodal multiparty model achieved 80% in accuracy. The experimental results show that multimodal models are better than unimodal models, and multiparty features contribute to improving the model performance. As an application of the proposed method, we implemented a meeting browser with persuasiveness visualization. The level of persuasiveness is visually indicated using the background color of each participant's timeline. Finally, we conducted a user study for our meeting browser, and found that the persuasiveness visualization helps the subjects grasp the flow of the discussion in a shorter time compared to a browser without persuasiveness visualization.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.34; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In group discussions, each participant has his/her own opinion, and if necessary, tries to persuade other participants to reach an agreement. Therefore, persuasiveness is an important skill for communicating with others. Based on this motivation, this study aims to estimate the persuasiveness of group discussion participants. First, human annotators rated the level of persuasiveness of each participant in group discussions among four people. We then created multimodal and multiparty models for estimating persuasiveness of a participant using speech, language, and visual (head pose) features using gated recurrent unit (GRU)-based neural network. In our experiment, in estimating the highest persuasive participant among the group, the performance of the proposed method was 76% in accuracy. In the binary classification task for estimating high or low persuasiveness participants among the group, the performance of the best performing multimodal multiparty model achieved 80% in accuracy. The experimental results show that multimodal models are better than unimodal models, and multiparty features contribute to improving the model performance. As an application of the proposed method, we implemented a meeting browser with persuasiveness visualization. The level of persuasiveness is visually indicated using the background color of each participant's timeline. Finally, we conducted a user study for our meeting browser, and found that the persuasiveness visualization helps the subjects grasp the flow of the discussion in a shorter time compared to a browser without persuasiveness visualization.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.34; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 64, 号 2, 発行日 2023-02-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会