Automatic Speech Recognition (ASR) may increase access to spoken information captured in videos. ASR is needed, especially for online academic video lectures that gradually replace class lectures and traditional textbooks. This conceptual article examines how technological barriers to ASR in under-resourced languages impair accessibility to video content and demonstrates it with the empirical findings of Hebrew ASR evaluations. We compare ASR with Optical Character Recognition (OCR) as facilitating access to textual and speech content and show their current performance in under-resourced languages. We target ASR of under-resourced languages as the main barrier to searching academic video lectures. We further show that information retrieval technologies, such as smart video players that combine both ASR and OCR capacities, must come to the fore once ASR technologies have matured. Therefore, suggesting that the current state of information retrieval from video lectures in under-resourced languages is equivalent to a knowledge dam.
|Number of pages||6|
|Journal||Journal of Computer Information Systems|
|State||Published - 2017|
Bibliographical noteFunding Information:
The authors gratefully acknowledge that this research was supported by the Open University of Israel’s research fund (grant no. 502532).
© 2017 International Association for Computer Information Systems.
Copyright 2017 Elsevier B.V., All rights reserved.
- Academic video lectures
- Automatic speech recognition (ASR)
- Optical character recognition (OCR)
- Under-resourced languages