Abstract
This paper presents a novel long-term idea to learn automatically from online multimedia content, such as videos from YouTube channels, a portfolio of nonverbal identities in the form of computational representation of prototypical gestures of a speaker. As a first step towards this vision, this paper presents proof-of-concept experiments to automatically identify visually accentuated words from a collection of online videos of the same person. The experimental results are promising with many accentuated words automatically identified and specific head motion patterns were associated with these words.
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Zadeh, A.B., Sagae, K., Morency, L.P. (2014). Towards Learning Nonverbal Identities from the Web: Automatically Identifying Visually Accentuated Words. In: Bickmore, T., Marsella, S., Sidner, C. (eds) Intelligent Virtual Agents. IVA 2014. Lecture Notes in Computer Science(), vol 8637. Springer, Cham. https://doi.org/10.1007/978-3-319-09767-1_60
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DOI: https://doi.org/10.1007/978-3-319-09767-1_60
Publisher Name: Springer, Cham
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