Automatic speech recognition: a survey
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 me...
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| Published in | Multimedia tools and applications Vol. 80; no. 6; pp. 9411 - 9457 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| 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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| AbstractList | 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. |
| Author | Malik, Mishaim Makhdoom, Imran Malik, Muhammad Kamran Mehmood, Khawar |
| Author_xml | – sequence: 1 givenname: Mishaim orcidid: 0000-0002-4917-7144 surname: Malik fullname: Malik, Mishaim email: mishaimmalik30@gmail.com organization: Punjab University College of Information Technology (PUCIT) – sequence: 2 givenname: Muhammad Kamran surname: Malik fullname: Malik, Muhammad Kamran organization: Faculty of Punjab University College of Information Technology (PUCIT) – sequence: 3 givenname: Khawar surname: Mehmood fullname: Mehmood, Khawar organization: School of Engineering and Information Technology, University of New South Wales (UNSW) Canberra at ADFA – sequence: 4 givenname: Imran surname: Makhdoom fullname: Makhdoom, Imran organization: Faculty of Engineering and IT, University of Technology Sydney |
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| Keywords | ASR Feature extraction Classification models Language models Speech recognition Automatic speech recognition |
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| Snippet | 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... |
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| SubjectTerms | Automatic speech recognition Computer Communication Networks Computer Science Data Structures and Information Theory Deep learning Feature extraction Language modeling Machine learning Multimedia Information Systems Special Purpose and Application-Based Systems Speech recognition Voice recognition |
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| Title | Automatic speech recognition: a survey |
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