Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Human computation
TurKit: human computation algorithms on mechanical turk
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
VizWiz: nearly real-time answers to visual questions
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Real-time crowd control of existing interfaces
Proceedings of the 24th annual ACM symposium on User interface software and technology
Crowds in two seconds: enabling realtime crowd-powered interfaces
Proceedings of the 24th annual ACM symposium on User interface software and technology
The design of human-powered access technology
The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility
Scribe4Me: evaluating a mobile sound transcription tool for the deaf
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Real-time captioning by groups of non-experts
Proceedings of the 25th annual ACM symposium on User interface software and technology
A readability evaluation of real-time crowd captions in the classroom
Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility
Real-time crowd labeling for deployable activity recognition
Proceedings of the 2013 conference on Computer supported cooperative work
Warping time for more effective real-time crowdsourcing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Adaptive time windows for real-time crowd captioning
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Accessibility Evaluation of Classroom Captions
ACM Transactions on Accessible Computing (TACCESS)
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Approaches for real-time captioning of speech are either expensive (professional stenographers) or error-prone (automatic speech recognition). As an alternative approach, we have been exploring whether groups of non-experts can collectively caption speech in real-time. In this approach, each worker types as much as they can and the partial captions are merged together in real-time automatically. This approach works best when partial captions are correct and received within a few seconds of when they were spoken, but these assumptions break down when engaging workers on-demand from existing sources of crowd work like Amazon's Mechanical Turk. In this paper, we present methods for quickly identifying workers who are producing good partial captions and estimating the quality of their input. We evaluate these methods in experiments run on Mechanical Turk in which a total of 42 workers captioned 20 minutes of audio. The methods introduced in this paper were able to raise overall accuracy from 57.8% to 81.22% while keeping coverage of the ground truth signal nearly unchanged.