Staff View: Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech

Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech

Depression has been affecting people all around the world, including Malaysians. Early detection mechanisms are vital for assisting clinical professionals in identifying depressed patients at an early stage. Although this can be accomplished through interviews and questionnaires, the time-consuming...

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Main Authors:	Shanmugam M., Ismail N.N.N., Magalingam P., Hashim N.N.W.N., Singh D.
Other Authors:	36195134500
Format:	Book chapter
Published:	Springer Science and Business Media Deutschland GmbH 2024
Subjects:	Acoustic measurement Depression Malay language Mel frequency cepstral coefficient (MFCC) Support vector machine (SVM)
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id	my.uniten.dspace-34496
record_format	dspace
spelling	my.uniten.dspace-344962024-10-14T11:20:11Z Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech Shanmugam M. Ismail N.N.N. Magalingam P. Hashim N.N.W.N. Singh D. 36195134500 58785152800 35302809600 57193675941 57683526600 Acoustic measurement Depression Malay language Mel frequency cepstral coefficient (MFCC) Support vector machine (SVM) Depression has been affecting people all around the world, including Malaysians. Early detection mechanisms are vital for assisting clinical professionals in identifying depressed patients at an early stage. Although this can be accomplished through interviews and questionnaires, the time-consuming method has several additional disadvantages. Acoustic Measurement and MFCC have notably been adapted to detect speaker emotion. Numerous researchers have employed various languages for the purpose of prediction. Its efficiency varies across research, although it contributes significantly to diagnosing depression. As it appears that culture diversity influences how emotion is perceived, depression detection mechanism can vary between different languages. This paper provides a comprehensive analysis based on relevant studies published from 2000 to 2023 to show the effectiveness of acoustic measurement and MFCC in depression detection. It was discovered that Support Vector Machine (SVM) is extensively utilised and can successfully contribute to the detection of depressed patients using biometric characteristics. The outcome of this study encourages experimental investigation on the effectiveness of acoustic measuring and MFCC for depression identification among Malaysian speakers. � The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. Final 2024-10-14T03:20:11Z 2024-10-14T03:20:11Z 2023 Book chapter 10.1007/978-3-031-48397-4_17 2-s2.0-85180912960 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180912960&doi=10.1007%2f978-3-031-48397-4_17&partnerID=40&md5=41cb7fa6aa92f6f13c51a66f6449c4ba https://irepository.uniten.edu.my/handle/123456789/34496 1128 345 359 Springer Science and Business Media Deutschland GmbH 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	Acoustic measurement Depression Malay language Mel frequency cepstral coefficient (MFCC) Support vector machine (SVM)
spellingShingle	Acoustic measurement Depression Malay language Mel frequency cepstral coefficient (MFCC) Support vector machine (SVM) Shanmugam M. Ismail N.N.N. Magalingam P. Hashim N.N.W.N. Singh D. Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech
description	Depression has been affecting people all around the world, including Malaysians. Early detection mechanisms are vital for assisting clinical professionals in identifying depressed patients at an early stage. Although this can be accomplished through interviews and questionnaires, the time-consuming method has several additional disadvantages. Acoustic Measurement and MFCC have notably been adapted to detect speaker emotion. Numerous researchers have employed various languages for the purpose of prediction. Its efficiency varies across research, although it contributes significantly to diagnosing depression. As it appears that culture diversity influences how emotion is perceived, depression detection mechanism can vary between different languages. This paper provides a comprehensive analysis based on relevant studies published from 2000 to 2023 to show the effectiveness of acoustic measurement and MFCC in depression detection. It was discovered that Support Vector Machine (SVM) is extensively utilised and can successfully contribute to the detection of depressed patients using biometric characteristics. The outcome of this study encourages experimental investigation on the effectiveness of acoustic measuring and MFCC for depression identification among Malaysian speakers. � The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
author2	36195134500
author_facet	36195134500 Shanmugam M. Ismail N.N.N. Magalingam P. Hashim N.N.W.N. Singh D.
format	Book chapter
author	Shanmugam M. Ismail N.N.N. Magalingam P. Hashim N.N.W.N. Singh D.
author_sort	Shanmugam M.
title	Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech
title_short	Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech
title_full	Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech
title_fullStr	Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech
title_full_unstemmed	Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech
title_sort	understanding the use of acoustic measurement and mel frequency cepstral coefficient (mfcc) features for the classification of depression speech
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2024
_version_	1814060100458905600
score	13.211869

Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech

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