An analysis of automatic transcription software – Work in Progress [181] Home : ALT Online Winter Conference 2018

An analysis of automatic transcription software – Work in Progress [181]

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Wed, Dec 12 2018, 4:00pm – 5:00pm

Authors: Tharindu Liyanagunawardena

Type: Webinar

Description

The use of video has increased dramatically over the years with the general reduction in cost of production and distribution. Today over 300 hours of video is being uploaded to YouTube every minute. The continued advancement of technological capabilities has enabled videos to be accessed easier than ever before on a variety of devices making it an increasingly popular medium for education.

This increased use of video in education as an instructional medium can be observed especially in online courses. Captioning and/or transcripts, make these resources accessible to people with disabilities and they also help all students including international learners. However, creating transcripts/captions manually can take lot of effort, both in terms of time and money.

Automatic Speech Recognition technologies have improved rapidly in the last couple of years. Both IBM and Microsoft have reached Word Error Rate (WER) almost in par with a professional human transcriber (Fogel, 2017; Lant, 2017). Transcript accuracy can be measured with WER and this is calculated using the formula: WER = (Substitution + Deletion + Insertions) / total number of words (Apone, Botkin, Brooks, & Goldberg, 2011).

Given the legal obligation to create accessible content it is important that new technologies and their capabilities are explored. In this presentation, I will be sharing some work in progress of assessing six automatic transcription services to explore how useful the current automatic transcription software could be in creating learning materials for the build environment sector.

References:

Apone, T., Botkin, B., Brooks, M., & Goldberg, L. (2011). Caption Accuracy Metrics Project: Research into Automated Error Ranking of Real-time Captions in Live Television News Programs. Retrieved from http://ncam.wgbh.org/file_download/136

Fogel, S. (3 October 2017). IBM inches toward human-like accuracy for speech recognition. Retrieved from https://www.engadget.com/2017/03/10/ibm-speech-recognition-accuracy-record/

Lant, K. (23 August 2017). Microsoft’s Speech Recognition is Now as Good as a Human Transcriber. Retrieved from https://futurism.com/microsofts-speech-recognition-is-now-as-good-as-a-human-transcriber

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Tharindu Liyanagunawardena posted an update in the session An analysis of automatic transcription software – Work in Progress [181] 4 years, 3 months ago

Hi there, if you want to see the slides used for the webinar they are available at https://www.slideshare.net/tharindu_slideshare/tharindu-alt-winter-conference-2018
ALT posted an update in the session An analysis of automatic transcription software – Work in Progress [181] 4 years, 3 months ago

A recording for this session is available from https://eu.bbcollab.com/recording/617ff44008c6469b8274062821c08ce7
Clare Dowzer joined the session An analysis of automatic transcription software – Work in Progress [181] 4 years, 3 months ago
alt_tompalmer joined the session An analysis of automatic transcription software – Work in Progress [181] 4 years, 3 months ago
Tom Foster joined the session An analysis of automatic transcription software – Work in Progress [181] 4 years, 3 months ago
sirhen joined the session An analysis of automatic transcription software – Work in Progress [181] 4 years, 3 months ago
Tharindu Liyanagunawardena joined the session An analysis of automatic transcription software – Work in Progress [181] 4 years, 3 months ago