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.

中文翻译：

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DeHumor：分解幽默的可视化分析

尽管幽默是一项重要的沟通技巧，但掌握幽默是具有挑战性的——成功地运用幽默需要结合引人入胜的内容构建和适当的声音传递（例如，停顿）。先前对计算幽默的研究强调紧邻妙语的文本和音频特征，但忽略了更长期的上下文设置。此外，这些理论通常过于抽象，无法理解每个具体的幽默片段。为了填补这一空白，我们开发了 DeHumor，这是一种可视化分析系统，用于分析公开演讲中的幽默行为。为了直观地揭示每个具体示例的构建块，DeHumor 将每个幽默视频分解为多模态特征，并在视频脚本上提供它们的内联注释。特别是，为了更好地捕捉堆积物，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，并在上下文链接图中将它们可视化。为了帮助用户找到具有想要学习的特征的妙语，我们在增强的时间矩阵上总结了内容（带有关键字）和幽默特征统计数据。通过单口喜剧节目和 TED 演讲的案例研究，我们表明 DeHumor 能够突出幽默示例的各种组成部分。此外，与沟通教练和幽默研究人员的专家访谈证明了 DeHumor 在对语音内容和声音传递进行多模态幽默分析方面的有效性。我们在增强的时间矩阵上总结了内容（带有关键字）和幽默特征统计数据。通过单口喜剧节目和 TED 演讲的案例研究，我们表明 DeHumor 能够突出幽默示例的各种组成部分。此外，与沟通教练和幽默研究人员的专家访谈证明了 DeHumor 在对语音内容和声音传递进行多模态幽默分析方面的有效性。我们在增强的时间矩阵上总结了内容（带有关键字）和幽默特征统计数据。通过单口喜剧节目和 TED 演讲的案例研究，我们表明 DeHumor 能够突出幽默示例的各种组成部分。此外，与沟通教练和幽默研究人员的专家访谈证明了 DeHumor 在对语音内容和声音传递进行多模态幽默分析方面的有效性。