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  1. Transactions on Accessible Computing (TACCESS)
  2. ACM Transactions on Accessible Computing (TACCESS) : Volume 5
  3. Issue 3, January 2014
  4. Accessibility Evaluation of Classroom Captions
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ACM Transactions on Accessible Computing (TACCESS) : Volume 9
ACM Transactions on Accessible Computing (TACCESS) : Volume 8
ACM Transactions on Accessible Computing (TACCESS) : Volume 7
ACM Transactions on Accessible Computing (TACCESS) : Volume 6
ACM Transactions on Accessible Computing (TACCESS) : Volume 5
Issue 4, March 2014
Issue 3, January 2014
Greetings from the New Editors-in-Chief
Accessibility Evaluation of Classroom Captions
Technology for Supporting Care Staff in Residential Homes
Issue 2, October 2013
Issue 1, September 2013
ACM Transactions on Accessible Computing (TACCESS) : Volume 4
ACM Transactions on Accessible Computing (TACCESS) : Volume 3
ACM Transactions on Accessible Computing (TACCESS) : Volume 2
ACM Transactions on Accessible Computing (TACCESS) : Volume 1

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Accessibility Evaluation of Classroom Captions

Content Provider ACM Digital Library
Author Bigham, Jeffrey P. Kushalnagar, Raja S. Lasecki, Walter S.
Copyright Year 2014
Abstract Real-time captioning enables deaf and hard of hearing (DHH) people to follow classroom lectures and other aural speech by converting it into visual text with less than a five second delay. Keeping the delay short allows end-users to follow and participate in conversations. This article focuses on the fundamental problem that makes real-time captioning difficult: sequential keyboard typing is much slower than speaking. We first surveyed the audio characteristics of 240 one-hour-long captioned lectures on YouTube, such as speed and duration of speaking bursts. We then analyzed how these characteristics impact caption generation and readability, considering specifically our human-powered collaborative captioning approach. We note that most of these characteristics are also present in more general domains. For our caption comparison evaluation, we transcribed a classroom lecture in real-time using all three captioning approaches. We recruited 48 participants (24 DHH) to watch these classroom transcripts in an eye-tracking laboratory. We presented these captions in a randomized, balanced order. We show that both hearing and DHH participants preferred and followed collaborative captions better than those generated by automatic speech recognition (ASR) or professionals due to the more consistent $\textit{flow}$ of the resulting captions. These results show the potential to reliably capture speech even during sudden bursts of speed, as well as for generating “enhanced” captions, unlike other human-powered captioning approaches.
Starting Page 1
Ending Page 24
Page Count 24
File Format PDF
ISSN 19367228
e-ISSN 19367236
DOI 10.1145/2543578
Volume Number 5
Issue Number 3
Journal ACM Transactions on Accessible Computing (TACCESS)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-01-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Real-time captioning Crowdsourcing Deaf Hard of hearing
Content Type Text
Resource Type Article
Subject Human-Computer Interaction Computer Science Applications
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