Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e.g., pause). Prior studies on computational humor emphasize the textual and audio features immediately next to the punchline, yet overlooking longer-term context setup. Moreover, the theories are usually too abstract for understanding each concrete humor snippet. To fill in the gap, we develop DeHumor, a visual analytical system for analyzing humorous behaviors in public speaking. To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features and provides inline annotations of them on the video script. In particular, to better capture the build-ups, we introduce content repetition as a complement to features introduced in theories of computational humor and visualize them in a context linking graph. To help users locate the punchlines that have the desired features to learn, we summarize the content (with keywords) and humor feature statistics on an augmented time matrix. With case studies on stand-up comedy shows and TED talks, we show that DeHumor is able to highlight various building blocks of humor examples. In addition, expert interviews with communication coaches and humor researchers demonstrate the effectiveness of DeHumor for multimodal humor analysis of speech content and vocal delivery.
翻译:尽管是关键的交流技巧,但抓住幽默是具有挑战性的 -- -- 成功使用幽默需要同时使用内容积累和适当的发声(例如暂停),先前的计算幽默研究强调紧靠拳击线的文字和音频特征,但忽略了长期背景设置。此外,理论通常过于抽象,无法理解每一个具体的幽默片段。要填补空白,我们开发DeHumor,一个用于分析公开演讲中幽默行为的视觉分析系统。要直观地揭示每个具体实例的构件,DeHumor将每个幽默视频分解成多式功能,并在视频脚本上提供它们的直线说明。特别是为了更好地捕捉到这些内容和音频特征,我们引入内容重复,作为计算幽默理论中引入的特征的补充,并在一个链接图中将其直观化。为了帮助用户定位具有理想特征的拳击线,我们用关键词总结了内容和幽默特征统计数据,在一个强化的时间矩阵中, DeHum 教练访谈能够展示各种幽默和幽默性分析,我们展示了演示和幽默性分析的组合。