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Abstract
Automatic voice pathology detection is a research topic, which has gained increasing interest recently. Although methods based on deep learning are becoming popular, the classical pipeline systems based on a two-stage architecture consisting of a feature extraction stage and a classifier stage are still widely used. In these classical detection systems, frame-wise computation of mel-frequency cepstral coefficients (MFCCs) is the most popular feature extraction method. However, no systematic study has been conducted to investigate the effect of the MFCC frame length on automatic voice pathology detection. In this work, we studied the effect of the MFCC frame length in voice pathology detection using three disorders (hyperkinetic dysphonia, hypokinetic dysphonia and reflux laryngitis) from the Saarbrûcken Voice Disorders (SVD) database. The detection performance was compared between speaker-dependent and speaker-independent scenarios as well as between speaking task -dependent and speaking task -independent scenarios. The Support Vector
Machine, which is the most widely used classifier in the study area, was used as the classifier. The results show that the detection accuracy depended on the MFFC frame length in all the scenarios studied. The best detection accuracy was obtained by using a MFFC frame length of 500 ms with a shift of 5 ms.
Machine, which is the most widely used classifier in the study area, was used as the classifier. The results show that the detection accuracy depended on the MFFC frame length in all the scenarios studied. The best detection accuracy was obtained by using a MFFC frame length of 500 ms with a shift of 5 ms.
Original language | English |
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Number of pages | 14 |
Journal | Journal of Voice |
DOIs | |
Publication status | E-pub ahead of print - 2022 |
MoE publication type | A1 Journal article-refereed |
Keywords
- voice pathology
- pathology detection
- speech analysis
- MFCC
- SVM
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Dive into the research topics of 'The Effect of the MFCC Frame Length in Automatic Voice Pathology Detection'. Together they form a unique fingerprint.Projects
- 1 Finished
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HEART: Speech-based biomarking of heart failure
Alku, P. (Principal investigator), Javanmardi, F. (Project Member), Mittapalle, K. (Project Member), Tirronen, S. (Project Member), Pohjalainen, H. (Project Member), Kodali, M. (Project Member), Yagnavajjula, M. (Project Member) & Kadiri, S. (Project Member)
01/09/2020 → 31/08/2024
Project: Academy of Finland: Other research funding