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Enhancement of Learning using Speech Recognition and Lecture Transcription: A Survey

Published on July 2014 by Ashwini B V, Laxmi B Rananavare
journal_cover_thumbnail

International Conference on Information and Communication Technologies
Foundation of Computer Science USA
ICICT - Number 6
July 2014
Authors: Ashwini B V, Laxmi B Rananavare
07332ca8-424d-4a3e-bba0-40a543b8a428

Ashwini B V, Laxmi B Rananavare . Enhancement of Learning using Speech Recognition and Lecture Transcription: A Survey. International Conference on Information and Communication Technologies. ICICT, 6 (July 2014), 6-11.

@article{
author = { Ashwini B V, Laxmi B Rananavare },
title = { Enhancement of Learning using Speech Recognition and Lecture Transcription: A Survey },
journal = { International Conference on Information and Communication Technologies },
issue_date = { July 2014 },
volume = { ICICT },
number = { 6 },
month = { July },
year = { 2014 },
issn = 0975-8887,
pages = { 6-11 },
numpages = 6,
url = { /proceedings/icict/number6/18004-1461/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Information and Communication Technologies
%A Ashwini B V
%A Laxmi B Rananavare
%T Enhancement of Learning using Speech Recognition and Lecture Transcription: A Survey
%J International Conference on Information and Communication Technologies
%@ 0975-8887
%V ICICT
%N 6
%P 6-11
%D 2014
%I International Journal of Computer Applications
Abstract

Speech recognition (SR) technologies were evaluated in different classroom environments to assist students to automatically convert oral lectures into text. Two distinct methods of SR-mediated lecture acquisition (SR-mLA), real-time captioning (RTC) and post-lecture transcription (PLT), has been developed to increase the word recognition accuracy. Both methods has been compared according to technical feasibility and reliability of classroom implementation, instructors' experiences, word recognition accuracy, and student class performance. RTC provided near-instantaneous display of the instructor's speech for students during class. PLT employed a user-independent SR algorithm to optimally generate multimedia class notes with synchronized lecture transcripts and instructor audio for students to access online after class. It has been learnt that PLT provides more word recognition accuracy than RTC. The potential benefits of SR-mLA for students who have difficulty taking notes accurately and independently were discussed, particularly for non-native English speakers and students with disabilities.

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Index Terms

Computer Science
Information Sciences

Keywords

Educational Technology Electronic Learning Multimedia Systems Notetaking Speech Recognition.