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Accessible lectures: moving towards automatic speech recognition models based on human methods

Published: 13 October 2008 Publication History

Abstract

The traditional lecture remains the most common method of teaching and while it is the most convenient from a delivery point of view, it is the least flexible and accessible. This paper responds to the challenge of meeting the needs and access requirements of students with disabilities by urging further adaptations in the learning environment. The aim of this work is to explore the way speech recognition technology can be employed in the University classroom to make lectures more flexible and accessible. The concluding section explores the concept of an ASR model, based on principles derived from studies of human methods of recognition, in order to increase their performance and efficiency.

References

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Bain, K., Basson, S. and Wald, M. 2002. Speech Recognition in University Classrooms: Liberated Learning Project. In Proceedings of the 5th International ACM Conference on Assistive Technologies (Edinburgh, Scotland, July 8--10, 2002). Assets '02. ACM, New York, NY, 192--196. DOI=http://doi.acm.org/10.1145/638249.638284
[2]
Demetriadis, S. and Pombortsis, A. 2007. e-Lectures for Flexible Learning: A Study on Their Learning Efficiency. Journal of Educational Technology & Society. 10, 2, 147--157.
[3]
Dusan, S. and Rabiner, L. 2005. Can Automatic Speech Recognition Learn More from Human Speech Perception. In Proceedings of the 3rd Romanian Academy Conference on Speech Technology and Human-Computer Dialogue (Cluj-Napoca, Romania, 2005). Romanian Academy.
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Kheir, R. and Way, T. 2007. Inclusion of Deaf Students in Computer Science Classes Using Real-Time Speech Transcription. In Proceedings of the 12th Annual SIGCSE Conference on Innovation & Technology in Computer Science Education (Dundee, Scotland, June 25--27, 2007). ITiCSE '07. ACM, New York, 192--196. DOI=http://doi.acm.org/10.1145/1269900.1268860
[5]
Leitch, D. and MacMillan, T. 2003. Liberated Learning Initiative Innovative Technology and Inclusion: Current Issues and Future Directions for Liberated Learning Research. Year III Report. Saint Mary's University, Nova Scotia.
[6]
Lickley, R., McKelvie, D. and Bard, E. 1999. Comparing Human & Automatic Speech Recognition Using Word Gating. In Proceedings of the ICPhS Satellite Meeting on Disfluency in Spontaneous Speech (UC Berkeley, California, July 30, 1999). 23--26.
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SENDA. 2001. Special Educational Needs And Disability Act. http://www.opsi.gov.uk/acts/acts2001/20010010.htm
[8]
Smith, A., Ling, P. and Hill, D. 2006. The Adoption of Multiple Modes of Delivery in Australian Universities. Journal of University Teaching and Learning Practice. 3, 2, 67--81.
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Wald, M. 2004. Using Automatic Speech Recognition to Enhance Education for All Students: Turning a Vision into Reality. In Proceedings of the 34th ASEE/IEEE Frontiers in Education Conference (Savannah, GA, October 20--23, 2004).

Cited By

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  • (2016)Improving Real-Time Captioning Experiences for Deaf and Hard of Hearing StudentsProceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/2982142.2982164(15-23)Online publication date: 23-Oct-2016

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  1. Accessible lectures: moving towards automatic speech recognition models based on human methods

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      cover image ACM Conferences
      Assets '08: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
      October 2008
      332 pages
      ISBN:9781595939760
      DOI:10.1145/1414471
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 13 October 2008

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      Author Tags

      1. accessibility
      2. automatic speech recognition (asr)
      3. human speech perception (hsp)

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      • (2016)Improving Real-Time Captioning Experiences for Deaf and Hard of Hearing StudentsProceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/2982142.2982164(15-23)Online publication date: 23-Oct-2016

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