VoiceCoach: Interactive Evidence-based Training for Voice Modulation Skills in Public Speaking

January 22, 2020 Β· Declared Dead Β· πŸ› International Conference on Human Factors in Computing Systems

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Authors Xingbo Wang, Haipeng Zeng, Yong Wang, Aoyu Wu, Zhida Sun, Xiaojuan Ma, Huamin Qu arXiv ID 2001.07876 Category cs.HC: Human-Computer Interaction Cross-listed cs.CL, cs.IR Citations 42 Venue International Conference on Human Factors in Computing Systems Last Checked 3 months ago
Abstract
The modulation of voice properties, such as pitch, volume, and speed, is crucial for delivering a successful public speech. However, it is challenging to master different voice modulation skills. Though many guidelines are available, they are often not practical enough to be applied in different public speaking situations, especially for novice speakers. We present VoiceCoach, an interactive evidence-based approach to facilitate the effective training of voice modulation skills. Specifically, we have analyzed the voice modulation skills from 2623 high-quality speeches (i.e., TED Talks) and use them as the benchmark dataset. Given a voice input, VoiceCoach automatically recommends good voice modulation examples from the dataset based on the similarity of both sentence structures and voice modulation skills. Immediate and quantitative visual feedback is provided to guide further improvement. The expert interviews and the user study provide support for the effectiveness and usability of VoiceCoach.
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