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

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dc.citedby0
dc.contributor.authorShanmugam M.en_US
dc.contributor.authorIsmail N.N.N.en_US
dc.contributor.authorMagalingam P.en_US
dc.contributor.authorHashim N.N.W.N.en_US
dc.contributor.authorSingh D.en_US
dc.contributor.authorid36195134500en_US
dc.contributor.authorid58785152800en_US
dc.contributor.authorid35302809600en_US
dc.contributor.authorid57193675941en_US
dc.contributor.authorid57683526600en_US
dc.date.accessioned2024-10-14T03:20:11Z
dc.date.available2024-10-14T03:20:11Z
dc.date.issued2023
dc.description.abstractDepression 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-48397-4_17
dc.identifier.epage359
dc.identifier.scopus2-s2.0-85180912960
dc.identifier.spage345
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85180912960&doi=10.1007%2f978-3-031-48397-4_17&partnerID=40&md5=41cb7fa6aa92f6f13c51a66f6449c4ba
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34496
dc.identifier.volume1128
dc.pagecount14
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleStudies in Computational Intelligence
dc.subjectAcoustic measurement
dc.subjectDepression
dc.subjectMalay language
dc.subjectMel frequency cepstral coefficient (MFCC)
dc.subjectSupport vector machine (SVM)
dc.titleUnderstanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speechen_US
dc.typeBook chapteren_US
dspace.entity.typePublication
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