FairTalk: Facilitating Balanced Participation in Video Conferencing by Implicit Visualization of Predicted Turn-Grabbing Intention
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912395372265472 |
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| author | Iijima, Ryo Yoshida, Shigeo Hashimoto, Atsushi Ma, Jiaxin |
| author_facet | Iijima, Ryo Yoshida, Shigeo Hashimoto, Atsushi Ma, Jiaxin |
| contents | Creating fair opportunities for all participants to contribute is a notable challenge in video conferencing. This paper introduces FairTalk, a system that facilitates the subconscious redistribution of speaking opportunities. FairTalk predicts participants' turn-grabbing intentions using a machine learning model trained on web-collected videoconference data with positive-unlabeled learning, where turn-taking detection provides automatic positive labels. To subtly balance speaking turns, the system visualizes predicted intentions by mimicking natural human behaviors associated with the desire to speak. A user study suggests that FairTalk may help improve speaking balance, though subjective feedback indicates no significant perceived impact. We also discuss design implications derived from participant interviews. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FairTalk: Facilitating Balanced Participation in Video Conferencing by Implicit Visualization of Predicted Turn-Grabbing Intention Iijima, Ryo Yoshida, Shigeo Hashimoto, Atsushi Ma, Jiaxin Human-Computer Interaction Creating fair opportunities for all participants to contribute is a notable challenge in video conferencing. This paper introduces FairTalk, a system that facilitates the subconscious redistribution of speaking opportunities. FairTalk predicts participants' turn-grabbing intentions using a machine learning model trained on web-collected videoconference data with positive-unlabeled learning, where turn-taking detection provides automatic positive labels. To subtly balance speaking turns, the system visualizes predicted intentions by mimicking natural human behaviors associated with the desire to speak. A user study suggests that FairTalk may help improve speaking balance, though subjective feedback indicates no significant perceived impact. We also discuss design implications derived from participant interviews. |
| title | FairTalk: Facilitating Balanced Participation in Video Conferencing by Implicit Visualization of Predicted Turn-Grabbing Intention |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.20138 |