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...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , , |
| Formato: | algorithm computer program equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/15/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149835975&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149835975 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/15/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149835975 149835975 149835975 10.1155/2021/6635964 149835975 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparative Analysis of CNN and RNN for Voice Pathology Detection. aug: au: Syed, Sidra Abid Rashid, Munaf Hussain, Samreen Zahid, Hira affil: Department of Biomedical Engineering and Department of Electrical Engineering, Ziauddin University Faculty of Engineering Science, Technology, and Management, Karachi, Pakistan sug: subj: Speech Disorders Diagnosis Acoustics Neural Networks (Computer) Human Speech Acoustics Acoustic Impedance Tests Software Comparative Studies Descriptive Statistics ab: 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. pubtype: Academic Journal doctype: algorithm computer program equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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