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Using Speech Recognition Transcription to enhance learning from lecture recordings

Using Speech Recognition Transcription to enhance learning from lecture recordings
Using Speech Recognition Transcription to enhance learning from lecture recordings
This paper explains how speech recognition captioning with collaborative editing provides affordable transcription/captioning of lecture recordings, supports inclusive learning, retention & recruitment and enables universities to comply with law. It considers how lecture recordings can be inclusively enhanced and what features in a lecture recording system would be beneficial for disabled students. It proposes that all university students learn better when they make their own notes rather than use notes made by somebody else and that a notetaker is not necessary when a time synchronised transcript and slides are available apart from for hearing impaired students who can’t check a recording to correct transcription errors. The paper provides evidence that speech recognition can be more accurate than human transcribers and that we should use students to collaboratively correct caption errors as commercial manual captioning is too expensive for universities.
111-115
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5

Wald, Michael (2018) Using Speech Recognition Transcription to enhance learning from lecture recordings. International Conference on Education and New Developments, , Budapest, Hungary. 23 - 25 Jun 2018. pp. 111-115 .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper explains how speech recognition captioning with collaborative editing provides affordable transcription/captioning of lecture recordings, supports inclusive learning, retention & recruitment and enables universities to comply with law. It considers how lecture recordings can be inclusively enhanced and what features in a lecture recording system would be beneficial for disabled students. It proposes that all university students learn better when they make their own notes rather than use notes made by somebody else and that a notetaker is not necessary when a time synchronised transcript and slides are available apart from for hearing impaired students who can’t check a recording to correct transcription errors. The paper provides evidence that speech recognition can be more accurate than human transcribers and that we should use students to collaboratively correct caption errors as commercial manual captioning is too expensive for universities.

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Accepted/In Press date: 2018
Published date: 2018
Venue - Dates: International Conference on Education and New Developments, , Budapest, Hungary, 2018-06-23 - 2018-06-25

Identifiers

Local EPrints ID: 419608
URI: http://eprints.soton.ac.uk/id/eprint/419608
PURE UUID: 4ecdb066-e052-4b88-a13b-1299958795b4

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Date deposited: 16 Apr 2018 16:30
Last modified: 05 Nov 2020 05:01

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