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Article published In:
Journal of Second Language Pronunciation
Vol. 5:2 (2019) ► pp.294–323

Developing and validating a methodology for crowdsourcing L2 speech ratings in Amazon Mechanical Turk

ORCID logo | Iowa State University
Researchers have increasingly turned to Amazon Mechanical Turk (AMT) to crowdsource speech data, predominantly in English. Although AMT and similar platforms are well positioned to enhance the state of the art in L2 research, it is unclear if crowdsourced L2 speech ratings are reliable, particularly in languages other than English. The present study describes the development and deployment of an AMT task to crowdsource comprehensibility, fluency, and accentedness ratings for L2 Spanish speech samples. Fifty-four AMT workers who were native Spanish speakers from 11 countries participated in the ratings. Intraclass correlation coefficients were used to estimate group-level interrater reliability, and Rasch analyses were undertaken to examine individual differences in rater severity and fit. Excellent reliability was observed for the comprehensibility and fluency ratings, but indices were slightly lower for accentedness, leading to recommendations to improve the task for future data collection.
Keywords: research methods, speech ratings, Spanish, reliability, many-facet Rasch measurement
Article outline
  • 1.Introduction
  • 2.Background
    • 2.1Online and laboratory L2 speech ratings
    • 2.2Demographics of AMT workers
    • 2.3The current study
  • 3.Method
    • 3.1Speech samples
    • 3.2Development and deployment of the AMT HITs
    • 3.3AMT workers
  • 4.Results
    • 4.1Attention checks and near-native control samples
    • 4.2Reliability
    • 4.3Rasch modeling
      • 4.3.1Calibration of speakers, raters, time, and tasks
      • 4.3.2Rater fit
      • 4.3.3Rating scale use and structure
    • 4.4Rater characteristics: Age, gender, and education
  • 5.Discussion
    • 5.1Data quality and reliability
    • 5.2Rater background
    • 5.3Recommendations for improving the AMT task
  • 6.Conclusion
  • Notes
  • References
Published online: 17 September 2019
DOI logo
https://doi.org/10.1075/jslp.18016.nag
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