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MFCC in audio signal processing for voice disorder: a review

Voice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction and classifier implementation techniques in identifying voice abnormalities has been investigated. Mel-Frequency Cepstral Coefficient (MFCC) has been e...

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Main Authors:	Sidhu M.S., Latib N.A.A., Sidhu K.K.
Other Authors:	56259597000
Format:	Article
Published:	Springer 2025
Subjects:	Classification (of information) Decision trees Extraction Feature extraction Neural networks Speech communication Speech recognition Audio signal Feature classifiers Feature extractor Features extraction Implementation techniques Mel frequency cepstral co-efficient Mel-frequency cepstral coefficients Support vectors machine Voice disorders Support vector machines
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id	my.uniten.dspace-37103
record_format	dspace
spelling	my.uniten.dspace-371032025-03-03T15:47:31Z MFCC in audio signal processing for voice disorder: a review Sidhu M.S. Latib N.A.A. Sidhu K.K. 56259597000 57224502095 57225456188 Classification (of information) Decision trees Extraction Feature extraction Neural networks Speech communication Speech recognition Audio signal Feature classifiers Feature extractor Features extraction Implementation techniques Mel frequency cepstral co-efficient Mel-frequency cepstral coefficients Support vectors machine Voice disorders Support vector machines Voice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction and classifier implementation techniques in identifying voice abnormalities has been investigated. Mel-Frequency Cepstral Coefficient (MFCC) has been extensively used as a feature extractor. This paper adopts a Comparative Review Method to assess the effectiveness of feature extraction and classifier methods in detecting voice disorders. By examining the pairing of the Mel-Frequency Cepstral Coefficient (MFCC) with various classifiers, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and other online or commercial classifiers, the study aims to review the robustness of MFCC in this context. The study also recognizes the significance of choosing the right database in light of the various aetiologies of pathological illnesses and its possible influence on the efficacy of voice disorder detection. ? The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Article in press 2025-03-03T07:47:31Z 2025-03-03T07:47:31Z 2024 Article 10.1007/s11042-024-19253-1 2-s2.0-85191709609 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191709609&doi=10.1007%2fs11042-024-19253-1&partnerID=40&md5=a6d83c6f0b1d6eebf6e3246f2e172474 https://irepository.uniten.edu.my/handle/123456789/37103 Springer Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Classification (of information) Decision trees Extraction Feature extraction Neural networks Speech communication Speech recognition Audio signal Feature classifiers Feature extractor Features extraction Implementation techniques Mel frequency cepstral co-efficient Mel-frequency cepstral coefficients Support vectors machine Voice disorders Support vector machines
spellingShingle	Classification (of information) Decision trees Extraction Feature extraction Neural networks Speech communication Speech recognition Audio signal Feature classifiers Feature extractor Features extraction Implementation techniques Mel frequency cepstral co-efficient Mel-frequency cepstral coefficients Support vectors machine Voice disorders Support vector machines Sidhu M.S. Latib N.A.A. Sidhu K.K. MFCC in audio signal processing for voice disorder: a review
description	Voice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction and classifier implementation techniques in identifying voice abnormalities has been investigated. Mel-Frequency Cepstral Coefficient (MFCC) has been extensively used as a feature extractor. This paper adopts a Comparative Review Method to assess the effectiveness of feature extraction and classifier methods in detecting voice disorders. By examining the pairing of the Mel-Frequency Cepstral Coefficient (MFCC) with various classifiers, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and other online or commercial classifiers, the study aims to review the robustness of MFCC in this context. The study also recognizes the significance of choosing the right database in light of the various aetiologies of pathological illnesses and its possible influence on the efficacy of voice disorder detection. ? The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
author2	56259597000
author_facet	56259597000 Sidhu M.S. Latib N.A.A. Sidhu K.K.
format	Article
author	Sidhu M.S. Latib N.A.A. Sidhu K.K.
author_sort	Sidhu M.S.
title	MFCC in audio signal processing for voice disorder: a review
title_short	MFCC in audio signal processing for voice disorder: a review
title_full	MFCC in audio signal processing for voice disorder: a review
title_fullStr	MFCC in audio signal processing for voice disorder: a review
title_full_unstemmed	MFCC in audio signal processing for voice disorder: a review
title_sort	mfcc in audio signal processing for voice disorder: a review
publisher	Springer
publishDate	2025
_version_	1825816083518455808
score	13.244109

MFCC in audio signal processing for voice disorder: a review

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