NDLI: ROC speak: semi-automated personalized feedback on nonverbal behavior from recorded videos

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ROC speak: semi-automated personalized feedback on nonverbal behavior from recorded videos

Content Provider	ACM Digital Library
Author	Jin, Yina Fung, Michelle Hoque, Mohammed Ehsan Zhao, RuJie
Abstract	We present a framework that couples computer algorithms with human intelligence in order to automatically sense and interpret nonverbal behavior. The framework is cloud-enabled and ubiquitously available via a web browser, and has been validated in the context of public speaking. The system automatically captures audio and video data in-browser through the user's webcam, and then analyzes the data for smiles, movement, and volume modulation. Our framework allows users to opt in and receive subjective feedback from Mechanical Turk workers ("Turkers"). Our system synthesizes the Turkers' interpretations, ratings, and comment rankings with the machine-sensed data and enables users to interact with, explore, and visualize personalized and presentational feedback. Our results provide quantitative and qualitative evidence in support of our proposed synthesized feedback, relative to video-only playback with impersonal tips. Our interface can be seen here: http://tinyurl.com/feedback-ui (Supported in Google Chrome.).
Starting Page	1167
Ending Page	1178
Page Count	12
File Format	PDF MP4
ISBN	9781450335744
DOI	10.1145/2750858.2804265
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Nonverbal behavior interpretation Automated multimodal affect sensing Public speaking Automated feedback
Content Type	Audio Text
Resource Type	Article

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