TY - GEN
T1 - Leveraging complementary contributions of different workers for efficient crowdsourcing of video captions
AU - Huang, Yun
AU - Huang, Yifeng
AU - Xue, Na
AU - Bigham, Jeffrey P.
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - 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.
AB - 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.
KW - Complementary contributions
KW - Crowdsourcing
KW - Video caption
UR - http://www.scopus.com/inward/record.url?scp=85019649862&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019649862&partnerID=8YFLogxK
U2 - 10.1145/3025453.3026032
DO - 10.1145/3025453.3026032
M3 - Conference contribution
AN - SCOPUS:85019649862
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 4617
EP - 4626
BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Y2 - 6 May 2017 through 11 May 2017
ER -