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Improving Multiple-Crowd-Sourced Transcriptions Using a Speech Recogniser


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Publication Date: 2015-05-12

Journal Title: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing

Publisher: IEEE

Pages: 4709-4713

Language: English

Type: Article

Metadata: Show full item record

Citation: van Dalen, R. C., Knill, K. M., Tsiakoulis, P., & Gales, M. J. F. (2015). Improving Multiple-Crowd-Sourced Transcriptions Using a Speech Recogniser. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 4709-4713.

Description: This is the accepted manuscript of a paper that will be published in the Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. It is currently under an infinite embargo.

Abstract: This paper introduces a method to produce high-quality transcrip- tions of speech data from only two crowd-sourced transcriptions. These transcriptions, produced cheaply by people on the Internet, for example through Amazon Mechanical Turk, are often of low qual- ity. Often, multiple crowd-sourced transcriptions are combined to form one transcription of higher quality. However, the state of the art is to use essentially a form of majority voting, which requires at least three transcriptions for each utterance. This paper shows how to refine this approach to work with only two transcriptions. It then introduces a method that uses a speech recogniser (bootstrapped on a simple combination scheme) to combine transcriptions. When only two crowd-sourced transcriptions are available, on a noisy data set this improves the word error rate to gold-standard transcriptions by 21 % relative.

Keywords: Automatic speech recognition, crowd-sourcing, transcription combination

Sponsorship: This paper reports on research supported by Cambridge English, University of Cambridge.

Identifiers:

This record's URL: http://www.repository.cam.ac.uk/handle/1810/247607http://dx.doi.org/10.1109/ICASSP.2015.7178864





Autor: van Dalen, R. C.Knill, K. M.Tsiakoulis, P.Gales, M. J. F.

Fuente: https://www.repository.cam.ac.uk/handle/1810/247607



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