Enhancing accessibility through correction of speech recognition errors

Abstract

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.