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Comparative Analysis of CNN and RNN for Voice Pathology Detection

Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to...

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Detalles Bibliográficos
Autores principales: Syed, Sidra Abid, Rashid, Munaf, Hussain, Samreen, Zahid, Hira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062167/
https://www.ncbi.nlm.nih.gov/pubmed/33937404
http://dx.doi.org/10.1155/2021/6635964
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author Syed, Sidra Abid
Rashid, Munaf
Hussain, Samreen
Zahid, Hira
author_facet Syed, Sidra Abid
Rashid, Munaf
Hussain, Samreen
Zahid, Hira
author_sort Syed, Sidra Abid
collection PubMed
description Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package.
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spelling pubmed-80621672021-04-29 Comparative Analysis of CNN and RNN for Voice Pathology Detection Syed, Sidra Abid Rashid, Munaf Hussain, Samreen Zahid, Hira Biomed Res Int Research Article Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package. Hindawi 2021-04-14 /pmc/articles/PMC8062167/ /pubmed/33937404 http://dx.doi.org/10.1155/2021/6635964 Text en Copyright © 2021 Sidra Abid Syed et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Syed, Sidra Abid
Rashid, Munaf
Hussain, Samreen
Zahid, Hira
Comparative Analysis of CNN and RNN for Voice Pathology Detection
title Comparative Analysis of CNN and RNN for Voice Pathology Detection
title_full Comparative Analysis of CNN and RNN for Voice Pathology Detection
title_fullStr Comparative Analysis of CNN and RNN for Voice Pathology Detection
title_full_unstemmed Comparative Analysis of CNN and RNN for Voice Pathology Detection
title_short Comparative Analysis of CNN and RNN for Voice Pathology Detection
title_sort comparative analysis of cnn and rnn for voice pathology detection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062167/
https://www.ncbi.nlm.nih.gov/pubmed/33937404
http://dx.doi.org/10.1155/2021/6635964
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