Syllable-based speech recognition using electromyography and decision set classifier
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2015
Članak u časopisu (Objavljena verzija)
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Prikaz svih podataka o dokumentuApstrakt
During the speech, contractions of muscles in the speech apparatus produce myoelectric signals that can be picked up by electrodes, filtered and analyzed. The problem of extraction of speech information from these signals is significant for patients with damaged speech apparatus, such as laryngectomy patients, who could use speech recognition based on myoelectric signal classification to communicate by means of the synthetic speech. In the most previously conducted research, classification is performed on a ten word vocabulary which resulted in a good classification rate. In this paper, a possibility for myoelectric syllable based speech classification is analyzed on a significantly larger vocabulary with novel decision set based classifier which is simple, easy to adapt, convenient for research and similar to the way humans think. In order to have a high quality of recorded myoelectric signals, analysis of the optimal position of electrodes is performed. classification is performed by... comparison between syllable combination and whole words. Based on classification rate, words can belong to easy, medium or hard to distinguish group. Results based on generated list of best matching combinations show that decision set analysis of myoelectric signals for speech recognition is a promising novel method.
Ključne reči:
Speech recognition / Electromyography / Decision set / Speech apparatus musclesIzvor:
Biomedical Engineering-Applications Basis Communications, 2015, 27, 2Izdavač:
- World Scientific Publ Co Pte Ltd, Singapore
Finansiranje / projekti:
DOI: 10.4015/S1016237215500209
ISSN: 1016-2372
WoS: 000365764400010
Scopus: 2-s2.0-84928485409
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Institucija/grupa
Geografski fakultetTY - JOUR AU - Topalović, Marko AU - Damnjanović, Đorđe AU - Peulić, Aleksandar AU - Blagojević, Milan AU - Filipović, Nenad PY - 2015 UR - https://gery.gef.bg.ac.rs/handle/123456789/708 AB - During the speech, contractions of muscles in the speech apparatus produce myoelectric signals that can be picked up by electrodes, filtered and analyzed. The problem of extraction of speech information from these signals is significant for patients with damaged speech apparatus, such as laryngectomy patients, who could use speech recognition based on myoelectric signal classification to communicate by means of the synthetic speech. In the most previously conducted research, classification is performed on a ten word vocabulary which resulted in a good classification rate. In this paper, a possibility for myoelectric syllable based speech classification is analyzed on a significantly larger vocabulary with novel decision set based classifier which is simple, easy to adapt, convenient for research and similar to the way humans think. In order to have a high quality of recorded myoelectric signals, analysis of the optimal position of electrodes is performed. classification is performed by comparison between syllable combination and whole words. Based on classification rate, words can belong to easy, medium or hard to distinguish group. Results based on generated list of best matching combinations show that decision set analysis of myoelectric signals for speech recognition is a promising novel method. PB - World Scientific Publ Co Pte Ltd, Singapore T2 - Biomedical Engineering-Applications Basis Communications T1 - Syllable-based speech recognition using electromyography and decision set classifier VL - 27 IS - 2 DO - 10.4015/S1016237215500209 UR - https://hdl.handle.net/21.15107/rcub_gery_708 ER -
@article{ author = "Topalović, Marko and Damnjanović, Đorđe and Peulić, Aleksandar and Blagojević, Milan and Filipović, Nenad", year = "2015", abstract = "During the speech, contractions of muscles in the speech apparatus produce myoelectric signals that can be picked up by electrodes, filtered and analyzed. The problem of extraction of speech information from these signals is significant for patients with damaged speech apparatus, such as laryngectomy patients, who could use speech recognition based on myoelectric signal classification to communicate by means of the synthetic speech. In the most previously conducted research, classification is performed on a ten word vocabulary which resulted in a good classification rate. In this paper, a possibility for myoelectric syllable based speech classification is analyzed on a significantly larger vocabulary with novel decision set based classifier which is simple, easy to adapt, convenient for research and similar to the way humans think. In order to have a high quality of recorded myoelectric signals, analysis of the optimal position of electrodes is performed. classification is performed by comparison between syllable combination and whole words. Based on classification rate, words can belong to easy, medium or hard to distinguish group. Results based on generated list of best matching combinations show that decision set analysis of myoelectric signals for speech recognition is a promising novel method.", publisher = "World Scientific Publ Co Pte Ltd, Singapore", journal = "Biomedical Engineering-Applications Basis Communications", title = "Syllable-based speech recognition using electromyography and decision set classifier", volume = "27", number = "2", doi = "10.4015/S1016237215500209", url = "https://hdl.handle.net/21.15107/rcub_gery_708" }
Topalović, M., Damnjanović, Đ., Peulić, A., Blagojević, M.,& Filipović, N.. (2015). Syllable-based speech recognition using electromyography and decision set classifier. in Biomedical Engineering-Applications Basis Communications World Scientific Publ Co Pte Ltd, Singapore., 27(2). https://doi.org/10.4015/S1016237215500209 https://hdl.handle.net/21.15107/rcub_gery_708
Topalović M, Damnjanović Đ, Peulić A, Blagojević M, Filipović N. Syllable-based speech recognition using electromyography and decision set classifier. in Biomedical Engineering-Applications Basis Communications. 2015;27(2). doi:10.4015/S1016237215500209 https://hdl.handle.net/21.15107/rcub_gery_708 .
Topalović, Marko, Damnjanović, Đorđe, Peulić, Aleksandar, Blagojević, Milan, Filipović, Nenad, "Syllable-based speech recognition using electromyography and decision set classifier" in Biomedical Engineering-Applications Basis Communications, 27, no. 2 (2015), https://doi.org/10.4015/S1016237215500209 ., https://hdl.handle.net/21.15107/rcub_gery_708 .