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Crowd translator: on building localized speech recognizers through micropayments

Authors:

Jonathan Ledlie,

Joseph PolifroniAuthors Info & Claims

ACM SIGOPS Operating Systems Review, Volume 43, Issue 4

Pages 84 - 89

https://doi.org/10.1145/1713254.1713273

Published: 27 January 2010 Publication History

Abstract

We present a method to expand the number of languages covered by simple speech recognizers. Enabling speech recognition in users' primary languages greatly extends the types of mobile-phone-based applications available to people in developing regions. We describe how we expand language corpora through user-supplied speech contributions, how we quickly evaluate each contribution, and how we pay contributors for their work.

References

[1]

Amazon Mechanical Turk. http://mturk.com.

[2]

D.P. Anderson, J. Cobb, E. Korpela, M. Lebofsky, and D. Werthimer. Seti@home. Communications of the ACM, 45(11):56--61, 2002.

Digital Library

[3]

Asterisk. http://asterisk.org.

[4]

J. Bernstein, K. Taussig, et al. MACROPHONE: An American English Telephone Speech Corpus for the Polyphone Project. In ICASSP, Apr. 1994.

[5]

R. Cole, M. Fanty, et al. Telephone speech corpus development at CSLU. In ICSLP, 1994.

[6]

P. Donmez, J.G. Carbonell, and J. Schneider. Efficiently Learning the Accuracy of Labeling Sources for Selective Sampling. In KDD, 2009.

Digital Library

[7]

N. Eagle. txteagle: Mobile Crowdsourcing. In HCII, July 2009.

Digital Library

[8]

GOOG-411. http://www.google.com/goog411.

[9]

E. Hurley, J. Polifroni, and J. Glass. Telephone data collection using the world wide web. In ICSLP, 1996.

[10]

A. Kathol, K. Precoda, D. Vergyri, W. Wang, and S. Riehemann. Speech Translation for Low-Resource Languages: The Case of Pashto. In Interspeech, 2005.

[11]

A. Kittur, E.H. Chi, and B. Suh. Crowdsourcing user studies with Mechanical Turk. In CHI, Apr. 2008.

Digital Library

[12]

I. Kruijff-Korbayová, K. Chvátalová, and O. Postolache. Annotation Guidelines for Czech-English Word Alignment. In LREC, 2006.

[13]

K. Laurila and P. Haavisto. Name dialing: How useful is it? In ICASSP, 2000.

Digital Library

[14]

J. Ledlie, N. Eagle, M. Tierney, M. Adler, H. Hansen, and J. Hicks. Mosoko: a Mobile Marketplace for Developing Regions. In DIS, Feb. 2008.

[15]

S. Narayanan et al. Speech Recognition Engineering Issues in Speech to Speech Translation System Design for Low Resource Languages and Domains. In ICASSP, 2006.

[16]

Nuance: OpenSpeech Recognizer. http://nuance.com.

[17]

L. Sarmenta. Sabotage-Tolerance Mechanisms for Volunteer Computing Systems. In CCGRID, May 2001.

Digital Library

[18]

V.S. Sheng, F. Provost, et al. Get another label? Improving data quality and data mining using multiple, noisy labelers. In KDD, Aug. 2008.

Digital Library

[19]

R. Snow, B. O'Connor, D. Jurafsky, and A.Y. Ng. Cheap and Fast -- But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks. In EMNLP, Oct. 2008.

Digital Library

[20]

S. Teller, J. Battat, B. Charrow, D. Curtis, R. Ryan, J. Ledlie, and J. Hicks. Organic Indoor Location Discovery. Tech. Report CSAIL TR-2008-075, MIT, Dec. 2008.

Cited By

Seaborn KSawa YWatanabe M(2024)Coimagining the Future of Voice Assistants with Cultural SensitivityHuman Behavior and Emerging Technologies10.1155/2024/32387372024(1-21)Online publication date: 25-Mar-2024
https://doi.org/10.1155/2024/3238737
Persaud AO'Brien S(2019)Quality and Acceptance of Crowdsourced Translation of Web ContentCrowdsourcing10.4018/978-1-5225-8362-2.ch043(881-897)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8362-2.ch043
Persaud AO'Brien S(2019)Quality and Acceptance of Crowdsourced Translation of Web ContentSocial Entrepreneurship10.4018/978-1-5225-8182-6.ch060(1177-1194)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8182-6.ch060
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Crowd translator: on building localized speech recognizers through micropayments

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Published In

cover image ACM SIGOPS Operating Systems Review

ACM SIGOPS Operating Systems Review Volume 43, Issue 4

January 2010

105 pages

ISSN:0163-5980

DOI:10.1145/1713254

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Copyright © 2010 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2010

Published in SIGOPS Volume 43, Issue 4

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Cited By

Seaborn KSawa YWatanabe M(2024)Coimagining the Future of Voice Assistants with Cultural SensitivityHuman Behavior and Emerging Technologies10.1155/2024/32387372024(1-21)Online publication date: 25-Mar-2024
https://doi.org/10.1155/2024/3238737
Persaud AO'Brien S(2019)Quality and Acceptance of Crowdsourced Translation of Web ContentCrowdsourcing10.4018/978-1-5225-8362-2.ch043(881-897)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8362-2.ch043
Persaud AO'Brien S(2019)Quality and Acceptance of Crowdsourced Translation of Web ContentSocial Entrepreneurship10.4018/978-1-5225-8182-6.ch060(1177-1194)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8182-6.ch060
Luo SSun YJi YZhao D(2018)Stackelberg Game Based Incentive Mechanisms for Multiple Collaborative Tasks in Mobile CrowdsourcingMobile Networks and Applications10.1007/s11036-015-0659-321:3(506-522)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s11036-015-0659-3
Persaud AO'Brien S(2017)Quality and Acceptance of Crowdsourced Translation of Web ContentInternational Journal of Technology and Human Interaction10.4018/IJTHI.201701010613:1(100-115)Online publication date: Jan-2017
https://doi.org/10.4018/IJTHI.2017010106
Vashistha ASethi PAnderson RMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)RespeakProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025640(1855-1866)Online publication date: 2-May-2017
https://dl.acm.org/doi/10.1145/3025453.3025640
Zhang ZCummins NSchuller B(2017)Advanced Data Exploitation in Speech Analysis: An overviewIEEE Signal Processing Magazine10.1109/MSP.2017.269935834:4(107-129)Online publication date: Jul-2017
https://doi.org/10.1109/MSP.2017.2699358
Huang YShema AXia H(2017)A proposed genome of mobile and situated crowdsourcing and its design implications for encouraging contributionsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2016.08.004102:C(69-80)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.ijhcs.2016.08.004
Luo SYu HLi L(2016)Decentralized deadline-aware coflow scheduling for datacenter networks2016 IEEE International Conference on Communications (ICC)10.1109/ICC.2016.7511251(1-6)Online publication date: May-2016
https://doi.org/10.1109/ICC.2016.7511251
Luo SSun YWen ZJi Y(2016)C2: Truthful incentive mechanism for multiple cooperative tasks in mobile cloud2016 IEEE International Conference on Communications (ICC)10.1109/ICC.2016.7511052(1-6)Online publication date: May-2016
https://doi.org/10.1109/ICC.2016.7511052
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