Automatic speech recognition: a survey

Malik, M; Malik, MK; Mehmood, K; Makhdoom, I

Automatic speech recognition: a survey

Malik, M Malik, MK Mehmood, K Makhdoom, I

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2021, 80, (6), pp. 9411-9457
Issue Date:: 2021-03-01

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Field	Value	Language
dc.contributor.author	Malik, M
dc.contributor.author	Malik, MK
dc.contributor.author	Mehmood, K
dc.contributor.author	Makhdoom, I https://orcid.org/0000-0002-6205-5897
dc.date.accessioned	2022-03-19T22:36:42Z
dc.date.available	2022-03-19T22:36:42Z
dc.date.issued	2021-03-01
dc.identifier.citation	Multimedia Tools and Applications, 2021, 80, (6), pp. 9411-9457
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/155370
dc.description.abstract	Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods. This study explores different feature extraction methods, state-of-the-art classification models, and vis-a-vis their impact on an ASR. As deep learning techniques are very data-dependent different speech datasets that are available online are also discussed in detail. In the end, the various online toolkits, resources, and language models that can be helpful in the formulation of an ASR are also proffered. In this study, we captured every aspect that can impact the performance of an ASR. Hence, we speculate that this work is a good starting point for academics interested in ASR research.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-020-10073-7
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	Software Engineering
dc.title	Automatic speech recognition: a survey
dc.type	Journal Article
utslib.citation.volume	80
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-19T22:36:40Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	80
utslib.citation.issue	6

Abstract:

Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods. This study explores different feature extraction methods, state-of-the-art classification models, and vis-a-vis their impact on an ASR. As deep learning techniques are very data-dependent different speech datasets that are available online are also discussed in detail. In the end, the various online toolkits, resources, and language models that can be helpful in the formulation of an ASR are also proffered. In this study, we captured every aspect that can impact the performance of an ASR. Hence, we speculate that this work is a good starting point for academics interested in ASR research.

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http://hdl.handle.net/10453/155370