A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture
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Date
2023-12-31
Authors
Amami, Rimah
Amami, Rim
Trabelsi, Chiraz
Mabrouk, Sherin Hassan
Khalil, Hassan A.
ORCID
Advisor
Referee
Mark
Journal Title
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Volume Title
Publisher
Institute of Automation and Computer Science, Brno University of Technology
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Abstract
Voice 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.
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Citation
Mendel. 2023 vol. 29, č. 2, s. 202-210. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/254
https://mendel-journal.org/index.php/mendel/article/view/254
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Peer-reviewed
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Language of document
en
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0
http://creativecommons.org/licenses/by-nc-sa/4.0