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The Study of Voice Pathology Detection based on MFCC and SVM

Authors:

Pengling RenAuthors Info & Claims

ICBBE '20: Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics Engineering

Pages 27 - 30

https://doi.org/10.1145/3444884.3444890

Published: 31 March 2021 Publication History

Abstract

Subjective auditory perception evaluation of voice is the most simple and direct method for judgment of the degree of voice lesions and the treatment effect. But it is closely related to the clinical experience of doctors. Recently, some voice automatic diagnosis methods based on voice feature parameters and classification algorithms have been proposed. Mel Frequency Cepstral Coefficient (MFCC) is the most commonly used feature parameter. However, it is not clear the role of MFCC dynamic features in improving diagnosis results. This study adopted the features of MFCC, MFCC + ΔMFCC, and MFCC + ΔMFCC + ΔΔMFCC respectively, combined with the Support Vector Machine (SVM) method to further determine whether adding dynamic MFCC features can improve the accuracy of pathological voice detection. The results showed that no matter whether dynamic features were added or not, the accuracy rate and specificity have not changed significantly. This means the dynamic change of the MFCC characteristic parameters is slight at least for vowel vocalization. This study may provide useful information for pathological voice diagnosis based on vowel vocalization.

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Cited By

S GPriya EM MSrinivas PS S(2024)Automated Voice Pathology Diagnosis Through Feature Extraction2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)10.1109/ICPECTS62210.2024.10780102(1-6)Online publication date: 8-Oct-2024
https://doi.org/10.1109/ICPECTS62210.2024.10780102

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ICBBE '20: Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics Engineering

November 2020

197 pages

ISBN:9781450388221

DOI:10.1145/3444884

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 March 2021

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Friendship Seeds Project of Beijing Friendship Hospital, Captial Medical University

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ICBBE '20

ICBBE '20: 2020 7th International Conference on Biomedical and Bioinformatics Engineering

November 6 - 9, 2020

Kyoto, Japan

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Cited By

S GPriya EM MSrinivas PS S(2024)Automated Voice Pathology Diagnosis Through Feature Extraction2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)10.1109/ICPECTS62210.2024.10780102(1-6)Online publication date: 8-Oct-2024
https://doi.org/10.1109/ICPECTS62210.2024.10780102

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