{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:10:05Z","timestamp":1749269405408,"version":"3.41.0"},"reference-count":45,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2025,6,1]]},"DOI":"10.1587\/transinf.2024hcp0008","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T22:12:48Z","timestamp":1737583968000},"page":"465-473","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Praising Behaviors Based on Multimodal Information"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Toshiki","family":"ONISHI","sequence":"first","affiliation":[{"name":"Graduate School of Integrated Basic Sciences, Nihon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asahi","family":"OGUSHI","sequence":"additional","affiliation":[{"name":"Graduate School of Integrated Basic Sciences, Nihon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryo","family":"ISHII","sequence":"additional","affiliation":[{"name":"NTT Human Informatics Laboratories, NTT Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akihiro","family":"MIYATA","sequence":"additional","affiliation":[{"name":"College of Humanities and Sciences, Nihon University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] L.N. Jenkins, M.T. Floress, and W. Reinke, \u201cRates and types of teacher praise: A review and future directions,\u201d Psychology in the Schools, vol.52, no.5, pp.463-476, 2015. 10.1002\/pits.21835","DOI":"10.1002\/pits.21835"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] J. Brophy, \u201cTeacher praise: A functional analysis,\u201d Review of educational research, vol.51, no.1, pp.5-32, 1981. 10.3102\/00346543051001005","DOI":"10.3102\/00346543051001005"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] T.M. Kalis, K.J. Vannest, and R. Parker, \u201cPraise counts: Using self-monitoring to increase effective teaching practices,\u201d Preventing School Failure: Alternative Education for Children and Youth, vol.51, no.3, pp.20-27, 2007. 10.3200\/psfl.51.3.20-27","DOI":"10.3200\/PSFL.51.3.20-27"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] S.H. Cheon, J. Reeve, T.H. Yu, and H.R. Jang, \u201cThe teacher benefits from giving autonomy support during physical education instruction,\u201d Journal of Sport and Exercise Psychology, vol.36, no.4, pp.331-346, 2014. 10.1123\/jsep.2013-0231","DOI":"10.1123\/jsep.2013-0231"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] B.P. Dor\u00e9, R.R. Morris, D.A. Burr, R.W. Picard, and K.N. Ochsner, \u201cHelping others regulate emotion predicts increased regulation of one\u2019s own emotions and decreased symptoms of depression,\u201d Personality and Social Psychology Bulletin, vol.43, no.5, pp.729-739, 2017. 10.1177\/0146167217695558","DOI":"10.1177\/0146167217695558"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] M.W. Beets, K.H. Pitetti, and L. Forlaw, \u201cThe role of self-efficacy and referent specific social support in promoting rural adolescent girls\u2019 physical activity,\u201d American journal of health behavior, vol.31, no.3, pp.227-237, 2007. 10.5993\/ajhb.31.3.1","DOI":"10.5993\/AJHB.31.3.1"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] K. Kakinuma, M. Nakai, Y. Hada, M. Kizawa, and A. Tanaka, \u201cPraise affects the \u201cpraiser\u201d: Effects of ability-focused vs. effort-focused praise on motivation,\u201d The Journal of Experimental Education, vol.90, no.3, pp.634-655, 2022. 10.1080\/00220973.2020.1799313","DOI":"10.1080\/00220973.2020.1799313"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] T. Onishi, A. Yamauchi, R. Ishii, Y. Aono, and A. Miyata, \u201cAnalyzing nonverbal behaviors along with praising,\u201d Proc. 22nd ACM International Conference on Multimodal Interaction (ICMI \u201920), pp.609-613, 2020. 10.1145\/3382507.3418868","DOI":"10.1145\/3382507.3418868"},{"key":"9","unstructured":"[9] T. Onishi, A. Yamauchi, A. Ogushi, R. Ishii, Y. Aono, and A. Miyata, \u201cAnalyzing head and face behaviors along with praising,\u201d IPSJ Journal, vol.62, no.9, pp.1620-1628, 2021. in Japanese."},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] T. Onishi, A. Yamauchi, A. Ogushi, R. Ishii, A. Fukayama, T. Nakamura, and A. Miyata, \u201cModeling japanese praising behavior by analyzing audio and visual behaviors,\u201d Frontiers in Computer Science, vol.4, 2022. 10.3389\/fcomp.2022.815128","DOI":"10.3389\/fcomp.2022.815128"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] A. Ogushi, T. Onishi, Y. Tahara, R. Ishii, A. Fukayama, T. Nakamura, and A. Miyata, \u201cAnalysis of praising skills focusing on utterance contents,\u201d Proc. 23rd Annual Conference of the International Speech Communication Association (INTERSPEECH \u201922), pp.2643-2747, 2022. 10.21437\/interspeech.2022-11200","DOI":"10.21437\/Interspeech.2022-11200"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] J. Henderlong and M.R. Lepper, \u201cThe effects of praise on children\u2019s intrinsic motivation: A review and synthesis,\u201d Psychological Bulletin, vol.128, no.5, pp.774-795, 2002. 10.1037\/\/0033-2909.128.5.774","DOI":"10.1037\/\/0033-2909.128.5.774"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] R.P. Ennis, D.J. Royer, K.L. Lane, H.M. Menzies, W.P. Oakes, and L.E. Schellman, \u201cBehavior-specific praise: An effective, efficient, low-intensity strategy to support student success,\u201d Beyond Behavior, vol.27, no.3, pp.134-139, 2018. 10.1177\/1074295618798587","DOI":"10.1177\/1074295618798587"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] R. Anderson, S.T. Manoogian, and J.S. Reznick, \u201cThe undermining and enhancing of intrinsic motivation in preschool children,\u201d Journal of personality and social psychology, vol.34, no.5, pp.915-922, 1976. 10.1037\/\/0022-3514.34.5.915","DOI":"10.1037\/\/0022-3514.34.5.915"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] S. D, R. S, and M. S., \u201cStudy2: 2-5-year-olds\u2019 reactions to success and failure and the effects of praise,\u201d Monographs of the Society for Research in Child Development, vol.57, no.1, pp.39-59, 1992. 10.1111\/j.1540-5834.1992.tb00292.x","DOI":"10.1111\/j.1540-5834.1992.tb00292.x"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] V. Ramanarayanan, C.W. Leong, L. Chen, G. Feng, and D. Suendermann-Oeft, \u201cEvaluating speech, face, emotion and body movement time-series features for automated multimodal presentation scoring,\u201d Proc. 17th ACM on International Conference on Multimodal Interaction (ICMI \u201915), pp.23-30, 2015. 10.1145\/2818346.2820765","DOI":"10.1145\/2818346.2820765"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] L. Chen, G. Feng, J. Joe, C.W. Leong, C. Kitchen, and C.M. Lee, \u201cTowards automated assessment of public speaking skills using multimodal cues,\u201d Proc. 16th International Conference on Multimodal Interaction (ICMI \u201914), pp.200-203, 2014. 10.1145\/2663204.2663265","DOI":"10.1145\/2663204.2663265"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] Y. Yagi, S. Okada, S. Shiobara, and S. Sugimura, \u201cPredicting multimodal presentation skills based on instance weighting domain adaptation,\u201d Journal on Multimodal User Interfaces, vol.16, no.1, pp.1-16, 2021. 10.1007\/s12193-021-00367-x","DOI":"10.1007\/s12193-021-00367-x"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] S. Park, H.S. Shim, M. Chatterjee, K. Sagae, and L.-P. Morency, \u201cComputational analysis of persuasiveness in social multimedia: A novel dataset and multimodal prediction approach,\u201d Proc. 16th International Conference on Multimodal Interaction (ICMI \u201914), pp.50-57, 2014. 10.1145\/2663204.2663260","DOI":"10.1145\/2663204.2663260"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] S. Okada, Y. Ohtake, Y.I. Nakano, Y. Hayashi, H.-H. Huang, Y. Takase, and K. Nitta, \u201cEstimating communication skills using dialogue acts and nonverbal features in multiple discussion datasets,\u201d Proc. 18th ACM International Conference on Multimodal Interaction (ICMI \u201916), pp.169-176, 2016. 10.1145\/2993148.2993154","DOI":"10.1145\/2993148.2993154"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] D. Sanchez-Cortes, O. Aran, M.S. Mast, and D. Gatica-Perez, \u201cA nonverbal behavior approach to identify emergent leaders in small groups,\u201d IEEE Trans. Multimedia, vol.14, no.3, pp.816-832, 2012. 10.1109\/tmm.2011.2181941","DOI":"10.1109\/TMM.2011.2181941"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] L.S. Nguyen, D. Frauendorfer, M.S. Mast, and D. Gatica-Perez, \u201cHire me: Computational inference of hirability in employment interviews based on nonverbal behavior,\u201d IEEE Trans. Multimedia, vol.16, no.4, pp.1018-1031, 2014. 10.1109\/tmm.2014.2307169","DOI":"10.1109\/TMM.2014.2307169"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] R. Ishii, K. Otsuka, S. Kumano, R. Higashinaka, and J. Tomita, \u201cAnalyzing gaze behavior and dialogue act during turn-taking for estimating empathy skill level,\u201d Proc. 20th ACM International Conference on Multimodal Interaction (ICMI \u201918), pp.31-39, 2018. 10.1145\/3242969.3242978","DOI":"10.1145\/3242969.3242978"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] Z.-X. Tan, A. Goel, T.-S. Nguyen, and D.C. Ong, \u201cA multimodal lstm for predicting listener empathic responses over time,\u201d OMG-Empathy Challenge workshop at the 14th IEEE International Conferenceon Automatic Face and Gesture Recognition (FG), pp.1-4, 2019. 10.1109\/fg.2019.8756577","DOI":"10.1109\/FG.2019.8756577"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] M. Soleymani, K. Stefanov, S.-H. Kang, J. Ondras, and J. Gratch, \u201cMultimodal analysis and estimation of intimate self-disclosure,\u201d Proc. 21st ACM International Conference on Multimodal Interaction (ICMI \u201919), pp.59-68, 2019. 10.1145\/3340555.3353737","DOI":"10.1145\/3340555.3353737"},{"key":"26","unstructured":"[26] T. Onishi, A. Ogushi, Y. Tahara, R. Ishii, A. Fukayama, T. Nakamura, and A. Miyata, \u201cA comparison of praising skills in face-to-face and remote dialogues,\u201d Proc. 13th Language Resources and Evaluation Conference (LREC \u201922), pp.5805-5812, 2022."},{"key":"27","unstructured":"[27] H. Brugman and A. Russel, \u201cAnnotating multimedia \/ multi-modal resources with elan,\u201d Proc. 4th International Conference on Language Resources and Language Evaluation (LREC \u201904), pp.2065-2068, 2004."},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] H. Koiso, Y. Horiuchi, S. Tutiya, A. Ichikawa, and Y. Den, \u201cAn analysis of turn-taking and backchannels based on prosodic and syntactic features in japanese map task dialogs,\u201d Language and Speech, vol.41, no.3-4, pp.295-321, 1998. 10.1177\/002383099804100404","DOI":"10.1177\/002383099804100404"},{"key":"29","unstructured":"[29] L. Huang, L.-P. Morency, and J. Gratch, \u201cParasocial consensus sampling: Combining multiple perspectives to learn virtual human behavior,\u201d Proc. 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS \u201910), pp.1265-1272, 2010."},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] K. Komatani, R. Takeda, and S. Okada, \u201cAnalyzing differences in subjective annotations by participants and third-party annotators in multimodal dialogue corpus,\u201d Proc. 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL \u201923), pp.104-113, 2023. 10.18653\/v1\/2023.sigdial-1.9","DOI":"10.18653\/v1\/2023.sigdial-1.9"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] R. Ishii, X. Ren, M. Muszynski, and L.-P. Morency, \u201cTrimodal prediction of speaking and listening willingness to help improve turn-changing modeling,\u201d Frontiers in Psychology, vol.13, 2022. 10.3389\/fpsyg.2022.774547","DOI":"10.3389\/fpsyg.2022.774547"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] L. Devillers, L. Vidrascu, and L. Lamel, \u201cChallenges in real-life emotion annotation and machine learning based detection,\u201d Neural Networks, vol.18, no.4, pp.407-422, 2005. 10.1016\/j.neunet.2005.03.007","DOI":"10.1016\/j.neunet.2005.03.007"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J.N. Chang, S. Lee, and S.S. Narayanan, \u201cIemocap: Interactive emotional dyadic motion capture database,\u201d Lang Resources &amp; Evaluation, vol.42, no.4, pp.335-359, 2008. 10.1007\/s10579-008-9076-6","DOI":"10.1007\/s10579-008-9076-6"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] D. Reidsma and R. op den Akker, \u201cExploiting \u2018subjective\u2019 annotations,\u201d Proc. Workshop on Human Judgements in Computational Linguistics (COLING \u201908), pp.8-16, 2008. 10.3115\/1611628.1611631","DOI":"10.3115\/1611628.1611631"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] S. Kumano, K. Otsuka, D. Mikami, M. Matsuda, and J. Yamato, \u201cAnalyzing interpersonal empathy via collective impressions,\u201d IEEE Transactions on Affective Computing, vol.6, no.4, pp.324-336, 2015. 10.1109\/taffc.2015.2417561","DOI":"10.1109\/TAFFC.2015.2417561"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] J.L. Fleiss, \u201cMeasuring nominal scale agreement among many raters,\u201d Psychological Bulletin, vol.76, no.5, pp.378-382, 1971. 10.1037\/h0031619","DOI":"10.1037\/h0031619"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] P. Ekman and W.V. Friesen, \u201cManual for the facial action coding system,\u201d Palo Alto: Consulting Psychologists Press, 1977.","DOI":"10.1037\/t27734-000"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] T. Baltru\u0161aitis, P. Robinson, and L.-P. Morency, \u201cOpenface: An open source facial behavior analysis toolkit,\u201d IEEE Winter Conference on Applications of Computer Vision (WACV \u201916), pp.1-10, 2016. 10.1109\/wacv.2016.7477553","DOI":"10.1109\/WACV.2016.7477553"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] F. Eyben, M. W`\u0300ollmer, and B. Schuller, \u201cOpensmile: the munich versatile and fast open-source audio feature extractor,\u201d Proc. 18th International Conference on Multimedia, pp.1459-1462, 2010. 10.1145\/1873951.1874246","DOI":"10.1145\/1873951.1874246"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] B. Schuller, S. Steidl, and A. Batliner, \u201cThe interspeech 2009 emotion challenge,\u201d Proc. Interspeech 2009, pp.312-315, 2009. 10.21437\/interspeech.2009-103","DOI":"10.21437\/Interspeech.2009-103"},{"key":"41","unstructured":"[41] T. Kudo, MeCab: yet another part-of-speech and morphological analyzer, 2006. https:\/\/taku910.github.io\/mecab\/."},{"key":"42","unstructured":"[42] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT \u201919), pp.4171-4186, 2019. 10.18653\/v1\/N19-1423"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] L. Breiman, \u201cRandom forests,\u201d Machine Learning, vol.45, no.1, pp.5-32, 2001. 10.1023\/a:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] J. Bergstra, D. Yamins, and D. Cox, \u201cHyperopt: A python library for optimizing the hyperparameters of machine learning algorithms,\u201d Proc. 12th Python in Science Conferences (SciPy \u201913), pp.13-20, 2013. 10.25080\/majora-8b375195-003","DOI":"10.25080\/Majora-8b375195-003"},{"key":"45","doi-asserted-by":"publisher","unstructured":"[45] G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing, \u201cLearning from class-imbalanced data: Review of methods and applications,\u201d Expert systems with applications, vol.73, pp.220-239, 2017. 10.1016\/j.eswa.2016.12.035","DOI":"10.1016\/j.eswa.2016.12.035"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/6\/E108.D_2024HCP0008\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T03:42:45Z","timestamp":1749267765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/6\/E108.D_2024HCP0008\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,1]]},"references-count":45,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024hcp0008","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,6,1]]},"article-number":"2024HCP0008"}}