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

• 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)

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.