Towards Learning Nonverbal Identities from the Web: Automatically Identifying Visually Accentuated Words.

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.