A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture

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dc.contributor.authorAmami, Rimah
dc.contributor.authorAmami, Rim
dc.contributor.authorTrabelsi, Chiraz
dc.contributor.authorMabrouk, Sherin Hassan
dc.contributor.authorKhalil, Hassan A.
dc.coverage.issue2cs
dc.coverage.volume29cs
dc.date.accessioned2024-01-11T09:48:05Z
dc.date.available2024-01-11T09:48:05Z
dc.date.issued2023-12-31cs
dc.description.abstractVoice recognition systems have become increasingly important in recent years due to the growing need for more efficient and intuitive human-machine interfaces. The use of Hybrid LSTM networks and deep learning has been very successful in improving speech detection systems. The aim of this paper is to develop a novel approach for the detection of voice pathologies using a hybrid deep learning model that combines the Bidirectional Long Short-Term Memory (BiLSTM) and the Convolutional Neural Network (CNN) architectures. The proposed model uses a combination of temporal and spectral features extracted from speech signals to detect the different types of voice pathologies. The performance of the proposed detection model is evaluated on a publicly available dataset of speech signals from individuals with various voice pathologies(MEEI database). The experimental results showed that the hybrid BiLSTM-CNN model outperforms several classifiers by achieving an accuracy of 98.86\%. The proposed model has the potential to assist health care professionals in the accurate diagnosis and treatment of voice pathologies, and improving the quality of life for affected individuals.en
dc.formattextcs
dc.format.extent202-210cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2023 vol. 29, č. 2, s. 202-210. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2023.2.202en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttps://hdl.handle.net/11012/244247
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/254cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectVoice Pathology Detectionen
dc.subjectConvolutional Neural Networken
dc.subjectBiLSTMen
dc.subjectHybrid Systemsen
dc.subjectMEEI Voice Disorders Databaseen
dc.titleA Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architectureen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 29, No. 2