Abstract

As a matter of fact, the system of human voice production is a sophisticated biological device that can modulate pitch and loudness. The essentials of internal and external factors often damage the vocal folds and change the vocal voice as a result. Thus, the consequences are well-portrayed in the function of the body and stand of emotion. Consequently, it is primary to identify voice changes at an early stage, deliver an opportunity to overcome any consequence, and enhance the patient's quality of life. In this case, voice disorder can be detected automatically by using Machine Learning (ML) techniques, which is, indeed, serves as a critical role. In this experiment, we specifically employ the Convolutional Neural Network (CNN), and a robust CNN model: the VGG-16. In investigating the performance of CNN in detecting disordered speech, we used the particular Pathological Voice Disorder (PVD) dataset, named the Respiratory Sound Database, which comprises hundreds of sampled PVD sound files. The experiment showed the accuracy of voice pathology detection arouses to 92.03%.

Original languageEnglish
Title of host publicationIBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages28-33
Number of pages6
ISBN (Electronic)9781728171562
DOIs
Publication statusPublished - 6 Oct 2020
Event37th International Conference on Biomedical Engineering, IBIOMED 2020 - Yogyakarta, Indonesia
Duration: 6 Oct 20208 Oct 2020

Publication series

NameIBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering

Conference

Conference37th International Conference on Biomedical Engineering, IBIOMED 2020
Country/TerritoryIndonesia
CityYogyakarta
Period6/10/208/10/20

Keywords

  • CNN
  • LSTM
  • Pathological Voice Disorder
  • VGG-16
  • VTLP Method

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