Creating accessible educational multimedia through editing automatic speech recognition captioning in real time

Abstract

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. Notetakers can only summarise what is being said while qualified sign language interpreters with a good understanding of the relevant higher education subject content are in very scarce supply. 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. Real time stenography transcription is not normally available in UK higher education because of the shortage of stenographers wishing to work in universities. Captions are time consuming and expensive to create by hand and while Automatic Speech Recognition can be used to provide real time captioning directly from lecturers’ speech in classrooms it has proved difficult to obtain accuracy comparable to stenography. This paper describes the development of a system that enables editors to correct errors in the captions as they are created by Automatic Speech Recognition.