Leveraging complementary contributions of different workers for efficient crowdsourcing of video captions

Yun Huang, Yifeng Huang, Na Xue, Jeffrey P. Bigham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Hearing-impaired people and non-native speakers rely on captions for access to video content, yet most videos remain uncaptioned or have machine-generated captions with high error rates. In this paper, we present the design, implementation and evaluation of BandCaption, a system that combines automatic speech recognition with input from crowd workers to provide a cost-efficient captioning solution for accessible online videos. We consider four stakeholder groups as our source of crowd workers: (i) individuals with hearing impairments, (ii) second-language speakers with low proficiency, (iii) second-language speakers with high proficiency, and (iv) native speakers. Each group has different abilities and incentives, which our workflow leverages. Our findings show that BandCaption enables crowd workers who have different needs and strengths to accomplish micro-tasks and make complementary contributions. Based on our results, we outline opportunities for future research and provide design suggestions to deliver cost-efficient captioning solutions.

Original languageEnglish (US)
Title of host publicationCHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery
Pages4617-4626
Number of pages10
Volume2017-May
ISBN (Electronic)9781450346559
DOIs
StatePublished - May 2 2017
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States
Duration: May 6 2017May 11 2017

Other

Other2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
CountryUnited States
CityDenver
Period5/6/175/11/17

Keywords

  • Complementary contributions
  • Crowdsourcing
  • Video caption

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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  • Cite this

    Huang, Y., Huang, Y., Xue, N., & Bigham, J. P. (2017). Leveraging complementary contributions of different workers for efficient crowdsourcing of video captions. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire (Vol. 2017-May, pp. 4617-4626). Association for Computing Machinery. https://doi.org/10.1145/3025453.3026032