Abstract
Nonverbal signals such as gazes, head nods, facial expressions, and bodily gestures play significant roles in organizing human interactions. Their significance is even more emphasized in multiparty settings, since many interaction organization behaviors, for example, turn-taking and participation role assignment, are realized nonverbally. Several projects have been involved in collecting multimodal corpora [3,4] for multiparty dialogues, to develop techniques for meeting event recognitions from nonverbal as well as verbal signals (e.g., [11,2]).
The task of annotating nonverbal signals exchanged in conversational interactions poses both theoretical and practical challenges for the development of multimodal corpora. Many projects rely on both manual annotation and automatic signal processing in corpus building. Some projects apply different methods to different types of signals to facilitate the efficient construction of corpora through the division of labor [9]. Others treat manual annotations as ideal values in the process of validating their signal processing methods [7].
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Matsusaka, Y., Katagiri, Y., Ishizaki, M., Enomoto, M. (2009). Unsupervised Clustering in Multimodal Multiparty Meeting Analysis. In: Kipp, M., Martin, JC., Paggio, P., Heylen, D. (eds) Multimodal Corpora. MMCorp 2008. Lecture Notes in Computer Science(), vol 5509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04793-0_6
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