Lectures can be digitally recorded and replayed to provide multimedia revision material for students who attended the class and a substitute learning experience for students unable to attend. Deaf and hard of hearing people can find it difficult to follow speech through hearing alone or to take notes while they are lip-reading or watching a sign-language interpreter. Synchronising the speech with text captions can ensure deaf students are not disadvantaged and assist all learners to search for relevant specific parts of the multimedia recording by means of the synchronised text. Automatic speech recognition has been used to provide real-time captioning directly from lecturers' speech in classrooms but it has proved difficult to obtain accuracy comparable to stenography. This paper describes the development, testing and evaluation of a system that enables editors to correct errors in the captions as they are created by automatic speech recognition and makes suggestions for future possible improvements.
The learning within lectures of hearing-impaired students can be hindered by errors in captions generated by speech recognition. My research intends to address this problem by investigating ways of correcting these captions. I summarise approaches to automatic error correction and describe the preliminary studies that have been conducted. These studies show that human editors set a tough benchmark for automatic correction to meet and indicate that automatic correction is feasible. Finally, I summarise my intention to develop a correction framework that will permit quantitative and qualitative testing of correction methods.
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