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This was a ALT Winter Conference Webinar on 12th December 2018
Webinar recording is available from https://eu.bbcollab.com/recording/617ff44008c6469b8274062821c08ce7
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.
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