Making chalk and talk accessible

Bennett, Steven; Hewitt, Jill; Kraithman, David; Britton, Carol

doi:10.1145/957205.957227

Cited by 10 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has also been noted elsewhere [3]. Informal investigations have suggested this might be because the rate of delivery varied more in a live classroom situation than in the office, resulting in the ends of words being run into the start of subsequent words.…”

Section: Accuracymentioning

confidence: 55%

Universal access to communication and learning: the role of automatic speech recognition

Wald

Bain

2007

Univ Access Inf Soc

View full text Add to dashboard Cite

This communication discusses how automatic speech recognition (ASR) can support universal access to communication and learning through the cost-effective production of text synchronised with speech and describes achievements and planned developments of the Liberated Learning Consortium to: support preferred learning and teaching styles; assist those who for cognitive, physical or sensory reasons find notetaking difficult; assist learners to manage and search online digital multimedia resources; provide automatic captioning of speech for deaf learners or when speech is not available or suitable; assist blind, visually impaired or dyslexic people to read and search material; and, assist speakers to improve their communication skills.

show abstract

Section: Accuracymentioning

confidence: 55%

Universal access to communication and learning: the role of automatic speech recognition

Wald

Bain

2007

Univ Access Inf Soc

View full text Add to dashboard Cite

show abstract

“…It was also observed that lecturers' ASR accuracy rates were lower in classes compared with those achieved in the office environment. This has also been noted elsewhere (Bennett et al, 2002). Informal investigations have suggested this might be because the rate of delivery varied more in a live classroom situation than in the office, resulting in the ends of words being run into the start of subsequent words.…”

Section: Accuracymentioning

confidence: 55%

An exploration of the potential of Automatic Speech Recognition to assist and enable receptive communication in higher education

Wald¹

2006

Research in Learning Technology

View full text Add to dashboard Cite

The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search learning material more readily by augmenting synthetic speech with natural recorded real speech is also discussed and evaluated. The automatic provision of online lecture notes, synchronised with speech, enables staff and students to focus on learning and teaching issues, while also benefiting learners unable to attend the lecture or who find it difficult or impossible to take notes at the same time as listening, watching and thinking.DOI: 10.1080/09687760500479977

show abstract

“…NaturallySpeaking can achieve impressive accuracy rates by trained speakers in controlled environments [2]. Figure 2 demonstrates the basic architecture of the system.…”

Section: B Modelling the Systemmentioning

confidence: 99%

Improving the editing process of automatically produced lecture transcripts based on Natural Language Analysis

Papadopoulos

Pearson

2010

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems

View full text Add to dashboard Cite

-Automatically produced lecture transcripts can act as an alternative to traditional note taking, benefiting those students whose needs and preferences are not met in the traditional learning environment. Nonetheless, despite the substantial progress that has been made in the area of Automatic Speech Recognition (ASR), the performance of ASR systems is still below the levels required for accurate transcription of lectures. This paper describes the development of a tool, which facilitates the evaluation of automatically produced transcription files, based on Natural Language Analysis. This tool is a step forward in the production of meaningful materials for disabled students, with minimal investment in time and effort by academic staff, thereby improving the accessibility of traditional teaching methodologies.

show abstract

Making chalk and talk accessible

Cited by 10 publications

References 2 publications

Universal access to communication and learning: the role of automatic speech recognition

Universal access to communication and learning: the role of automatic speech recognition

An exploration of the potential of Automatic Speech Recognition to assist and enable receptive communication in higher education

Improving the editing process of automatically produced lecture transcripts based on Natural Language Analysis

Contact Info

Product

Resources

About