@article{oai:ipsj.ixsq.nii.ac.jp:00225619,
 author = {Shunpei, Yamaguchi and Motoki, Nagano and Ritsuko, Oshima and Jun, Oshima and Takuya, Fujihashi and Shunsuke, Saruwatari and Takashi, Watanabe and Shunpei, Yamaguchi and Motoki, Nagano and Ritsuko, Oshima and Jun, Oshima and Takuya, Fujihashi and Shunsuke, Saruwatari and Takashi, Watanabe},
 issue = {2},
 journal = {情報処理学会論文誌デジタルプラクティス（TDP）},
 month = {Apr},
 note = {Collaborative learning fosters the ability to creatively solve problems in collaboration with other learners. Researchers in learning science have transcribed learners' speech to qualitatively analyze collaborative learning to reveal various patterns that increase learning performance. Although prior studies have identified speakers to support the process of transcription, those studies were limited in simultaneously identifying multiple speakers. We propose a novel speaker-identification algorithm that can simultaneously recognize multiple speakers using business-card-type sensors. The algorithm can remove ambient noise with low-cost sensors and still identify multiple simultaneous speakers. The experimental evaluations show that the algorithm accurately identifies simultaneous multiple speakers in a multi-person activity under conditions with varying numbers of users, environmental noise, and users' short utterances.
------------------------------
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)
------------------------------, Collaborative learning fosters the ability to creatively solve problems in collaboration with other learners. Researchers in learning science have transcribed learners' speech to qualitatively analyze collaborative learning to reveal various patterns that increase learning performance. Although prior studies have identified speakers to support the process of transcription, those studies were limited in simultaneously identifying multiple speakers. We propose a novel speaker-identification algorithm that can simultaneously recognize multiple speakers using business-card-type sensors. The algorithm can remove ambient noise with low-cost sensors and still identify multiple simultaneous speakers. The experimental evaluations show that the algorithm accurately identifies simultaneous multiple speakers in a multi-person activity under conditions with varying numbers of users, environmental noise, and users' short utterances.
------------------------------
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)
------------------------------},
 title = {Multi-Speaker Identification with IoT Badges for Collaborative Learning Analysis},
 volume = {4},
 year = {2023}
}