Abstract
Crowdsourcing is a powerful tool for massive transcription at a relatively low cost, since the transcription effort is distributed into a set of collaborators, and therefore, supervision effort of professional transcribers may be dramatically reduced. Nevertheless, collaborators are a scarce resource, which makes optimisation very important in order to get the maximum benefit from their efforts. In this work, the optimisation of the work load in the side of collaborators is studied in a multimodal crowdsourcing platform where speech dictation of handwritten text lines is used as transcription source. The experiments explore how this optimisation allows to obtain similar results reducing the number of collaborators and the number of text lines that they have to read.
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Acknowledgments
Work partially supported by projects SmartWays - RTC-2014-1466-4 (MINECO) and CoMUN-HaT - TIN2015-70924-C2-1-R (MINECO/FEDER).
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Granell, E., Martínez-Hinarejos, CD. (2016). Collaborator Effort Optimisation in Multimodal Crowdsourcing for Transcribing Historical Manuscripts. In: Abad, A., et al. Advances in Speech and Language Technologies for Iberian Languages. IberSPEECH 2016. Lecture Notes in Computer Science(), vol 10077. Springer, Cham. https://doi.org/10.1007/978-3-319-49169-1_23
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