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Business model for captioning university lecture recordings

Business model for captioning university lecture recordings
Business model for captioning university lecture recordings
Deaf and Hard of Hearing people can benefit from captioning of video recordings and transcription of audio recordings and guidelines (e.g. Web Content Accessibility Guidelines (WCAG 2.0)) or legislation in many countries has been drafted to encourage or require organisations to caption and transcribe their recordings. Captioning and transcription by qualified and trained professionals is expensive and not affordable in many countries and situations (e.g. University Lectures). Captions and transcriptions can also be of great benefit to non-native speakers or when there is a high level of background noise or there is no audio available (e.g. TV screens in public places such as airports) and also allow video and audio recordings to be searched. Professional captioning is expensive and crowdsourcing may reduce the costs but requires comparisons to verify the accuracy of the work and so as the number of crowdsourced captioners is increased to increase accuracy, the cost is also increased. Automatic Speech Recognition can be used to automatically caption and transcribe recordings but requires human correction of errors. This paper compares captioning methods and costs and suggests an affordable model might be to use students to correct automatic speech recognition errors of their lecture recordings.
2392-5523
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5

Wald, Michael (2017) Business model for captioning university lecture recordings. Journal of Manangement Science, Suratthani Rajabhat University, 3.

Record type: Article

Abstract

Deaf and Hard of Hearing people can benefit from captioning of video recordings and transcription of audio recordings and guidelines (e.g. Web Content Accessibility Guidelines (WCAG 2.0)) or legislation in many countries has been drafted to encourage or require organisations to caption and transcribe their recordings. Captioning and transcription by qualified and trained professionals is expensive and not affordable in many countries and situations (e.g. University Lectures). Captions and transcriptions can also be of great benefit to non-native speakers or when there is a high level of background noise or there is no audio available (e.g. TV screens in public places such as airports) and also allow video and audio recordings to be searched. Professional captioning is expensive and crowdsourcing may reduce the costs but requires comparisons to verify the accuracy of the work and so as the number of crowdsourced captioners is increased to increase accuracy, the cost is also increased. Automatic Speech Recognition can be used to automatically caption and transcribe recordings but requires human correction of errors. This paper compares captioning methods and costs and suggests an affordable model might be to use students to correct automatic speech recognition errors of their lecture recordings.

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More information

Accepted/In Press date: 1 January 2017
Published date: 11 January 2017
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 406632
URI: http://eprints.soton.ac.uk/id/eprint/406632
ISSN: 2392-5523
PURE UUID: 235c1661-97bf-4669-a4fb-a3404cf4b157

Catalogue record

Date deposited: 18 Mar 2017 02:25
Last modified: 06 Oct 2020 16:42

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