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Automatic temporal ranking of children's engagement levels using multi-modal cues.

, , and . Comput. Speech Lang., (2018)

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Selecting the right robot: Influence of user attitude, robot sociability and embodiment on user preferences., and . RO-MAN, page 682-687. IEEE, (2015)Automatic Recognition of Spontaneous Emotions in Speech Using Acoustic and Lexical Features., and . MLMI, volume 5237 of Lecture Notes in Computer Science, page 161-172. Springer, (2008)"You Can Do It!" - Crowdsourcing Motivational Speech and Text Messages., , , and . Macrotask Crowdsourcing, Springer, (2019)Multimodal Detection of Engagement in Groups of Children Using Rank Learning., , , , , and . HBU, volume 9997 of Lecture Notes in Computer Science, page 35-48. (2016)Towards modeling expressed emotions in oral history interviews: Using verbal and nonverbal signals to track personal narratives., , , and . Lit. Linguistic Comput., 29 (4): 621-636 (2014)Nanogami: the microbiome expanded. speak your truth. listen to your gut., , , , and . UbiComp, page 295-300. ACM, (2018)An annotation scheme for sighs in spontaneous dialogue., , , and . INTERSPEECH, page 228-232. ISCA, (2014)Automatic detection of frequent pronunciation errors made by L2-learners., , , , and . INTERSPEECH, page 1345-1348. ISCA, (2005)Arousal and valence prediction in spontaneous emotional speech: felt versus perceived emotion., , , and . INTERSPEECH, page 2027-2030. ISCA, (2009)Robot-Supported Information Search: Which Conversational Interaction Style do Children Prefer?, , and . HRI (Companion), page 466-470. ACM, (2023)