AlSaleh, M.M., Arvaneh, M. orcid.org/0000-0002-5124-3497, Christensen, H. orcid.org/0000-0003-3028-5062 et al. (1 more author) (2016) Brain-computer interface technology for speech recognition: A review. In: Proceedings of 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 13-16 Dec 2016, Jeju, South Korea. IEEE , pp. 1-5. ISBN 9781509024018
Abstract
This paper presents an overview of the studies that have been conducted with the purpose of understanding the use of brain signals as input to a speech recogniser. The studies have been categorised based on the type of the technology used with a summary of the methodologies used and achieved results. In addition, the paper gives an insight into some studies that examined the effect of the chosen stimuli on brain activities as an important factor in the recognition process. The remaining part of this paper lists the limitations of the available studies and the challenges for future work in this area.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Speech; Speech recognition; Electroencephalography; Brain; Production; Support vector machines; Hidden Markov models |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Oct 2020 13:48 |
Last Modified: | 27 Oct 2020 13:57 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/apsipa.2016.7820826 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167273 |