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Multimodal Data Fusion of Electromyography and Acoustic Signals for Thai Syllable Recognition

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dc.contributor.authorSae Jong, Nida
dc.contributor.authorGarcía Seco de Herrera, Alba
dc.date.accessioned2025-03-27T08:56:38Z
dc.date.available2025-03-27T08:56:38Z
dc.date.issued2020-10-27
dc.descriptionEsta es la versión aceptada del artículo. La versión registrada fue publicada por primera vez en IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 1997-2006, está disponible en línea en el sitio web del editor: IEEE Xplore, https://doi.org/10.1109/JBHI.2020.3034158. This is the accepted version of the article. The registered version was first published in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 1997-2006, is available online at the publisher's website: IEEE Xplore, https://doi.org/10.1109/JBHI.2020.3034158.
dc.description.abstractSpeech disorders such as dysarthria are common and frequent after suffering a stroke. Speech rehabilitation performed by a speech-language pathologist is needed to improve and recover. However, in Thailand, there is a shortage of speech-language pathologists. In this paper, we present a syllable recognition system, which can be deployable in a speech rehabilitation system to provide support to the limited speech-language pathologists available. The proposed system is based on a multimodal fusion of acoustic signal and surface electromyography (sEMG) collected from facial muscles. Multimodal data fusion is studied to improve signal collection under noisy situations while reducing the number of electrodes needed. The signals are simultaneously collected while articulating 12 Thai syllables designed for rehabilitation exercises. Several features are extracted from sEMG signals and five channels are studied. The best combination of features and channels is chosen to be fused with the mel-frequency cepstral coefficients extracted from the acoustic signal. The feature vector from each signal source is projected by spectral regression extreme learning machine and concatenated. Data from seven healthy subjects were collected for evaluation purposes. Results show that the multimodal fusion outperforms the use of a single signal source achieving up to ~ 98% of accuracy. In other words, an accuracy improvement up to 5% can be achieved when using the proposed multimodal fusion. Moreover, its low standard deviations in classification accuracy compared to those from the unimodal fusion indicate the improvement in the robustness of the syllable recognition.en
dc.description.versionversión final
dc.identifier.citationN. S. Jong, A. G. S. de Herrera and P. Phukpattaranont, "Multimodal Data Fusion of Electromyography and Acoustic Signals for Thai Syllable Recognition," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 1997-2006, June 2021, https://doi.org/10.1109/JBHI.2020.3034158.
dc.identifier.doihttps://doi.org/10.1109/JBHI.2020.3034158
dc.identifier.issn2168-2194; eISSN: 2168-2208
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26372
dc.journal.issue6
dc.journal.titleIEEE Journal of Biomedical and Health Informatics
dc.journal.volume25
dc.language.isoen
dc.page.final2006
dc.page.initial1997
dc.publisherIEEE_ Institute of Electrical and Electronics Engineers
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsAcoustic signalen
dc.subject.keywordselectromyographyen
dc.subject.keywordsfeature-level fusionen
dc.subject.keywordsmultimodal fusionen
dc.subject.keywordsspeech recognitionen
dc.titleMultimodal Data Fusion of Electromyography and Acoustic Signals for Thai Syllable Recognitionen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
relation.isAuthorOfPublication.latestForDiscovery33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
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