Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review
Voice is one of the essential mechanisms for communicating and expressing one’s intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/4/1399 |
| _version_ | 1797396234541989888 |
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| author | Wookey Lee Jessica Jiwon Seong Busra Ozlu Bong Sup Shim Azizbek Marakhimov Suan Lee |
| author_facet | Wookey Lee Jessica Jiwon Seong Busra Ozlu Bong Sup Shim Azizbek Marakhimov Suan Lee |
| author_sort | Wookey Lee |
| collection | DOAJ |
| description | Voice is one of the essential mechanisms for communicating and expressing one’s intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase. Novel approaches should have been developed for speech recognition and production because that would seriously undermine the quality of life and sometimes leads to isolation from society. In this review, we survey mouth interface technologies which are mouth-mounted devices for speech recognition, production, and volitional control, and the corresponding research to develop artificial mouth technologies based on various sensors, including electromyography (EMG), electroencephalography (EEG), electropalatography (EPG), electromagnetic articulography (EMA), permanent magnet articulography (PMA), gyros, images and 3-axial magnetic sensors, especially with deep learning techniques. We especially research various deep learning technologies related to voice recognition, including visual speech recognition, silent speech interface, and analyze its flow, and systematize them into a taxonomy. Finally, we discuss methods to solve the communication problems of people with disabilities in speaking and future research with respect to deep learning components. |
| first_indexed | 2024-03-09T00:48:26Z |
| format | Article |
| id | doaj.art-41860ce31bdf4d51b50a9b640810657f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:48:26Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-41860ce31bdf4d51b50a9b640810657f2023-12-11T17:23:07ZengMDPI AGSensors1424-82202021-02-01214139910.3390/s21041399Biosignal Sensors and Deep Learning-Based Speech Recognition: A ReviewWookey Lee0Jessica Jiwon Seong1Busra Ozlu2Bong Sup Shim3Azizbek Marakhimov4Suan Lee5Biomedical Science and Engineering & Dept. of Industrial Security Governance & IE, Inha University, 100 Inharo, Incheon 22212, KoreaDepartment of Industrial Security Governance, Inha University, 100 Inharo, Incheon 22212, KoreaBiomedical Science and Engineering & Department of Chemical Engineering, Inha University, 100 Inharo, Incheon 22212, KoreaBiomedical Science and Engineering & Department of Chemical Engineering, Inha University, 100 Inharo, Incheon 22212, KoreaFrontier College, Inha University, 100 Inharo, Incheon 22212, KoreaSchool of Computer Science, Semyung University, Jecheon 27136, KoreaVoice is one of the essential mechanisms for communicating and expressing one’s intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase. Novel approaches should have been developed for speech recognition and production because that would seriously undermine the quality of life and sometimes leads to isolation from society. In this review, we survey mouth interface technologies which are mouth-mounted devices for speech recognition, production, and volitional control, and the corresponding research to develop artificial mouth technologies based on various sensors, including electromyography (EMG), electroencephalography (EEG), electropalatography (EPG), electromagnetic articulography (EMA), permanent magnet articulography (PMA), gyros, images and 3-axial magnetic sensors, especially with deep learning techniques. We especially research various deep learning technologies related to voice recognition, including visual speech recognition, silent speech interface, and analyze its flow, and systematize them into a taxonomy. Finally, we discuss methods to solve the communication problems of people with disabilities in speaking and future research with respect to deep learning components.https://www.mdpi.com/1424-8220/21/4/1399mouth interfacevoice productionartificial larynxEMGbiosignaldeep learning |
| spellingShingle | Wookey Lee Jessica Jiwon Seong Busra Ozlu Bong Sup Shim Azizbek Marakhimov Suan Lee Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review Sensors mouth interface voice production artificial larynx EMG biosignal deep learning |
| title | Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review |
| title_full | Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review |
| title_fullStr | Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review |
| title_full_unstemmed | Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review |
| title_short | Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review |
| title_sort | biosignal sensors and deep learning based speech recognition a review |
| topic | mouth interface voice production artificial larynx EMG biosignal deep learning |
| url | https://www.mdpi.com/1424-8220/21/4/1399 |
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