Enhancing stress speech classification through the fusion of emotional datasets utilizing MFCCs with CNN

Zainal, Nur Aishah and Asnawi, Ani Liza (2024) Enhancing stress speech classification through the fusion of emotional datasets utilizing MFCCs with CNN. In: 2024 9th International Conferences on Mechatronics (ICOM), 13-14 August 2024, Kuala Lumpur.

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Abstract

Stress classification involves categorizing an individual's perceived stress state. One approach involves analyzing human speech due to its non-invasive nature, offering advantages over traditional methods requiring intrusive procedures. Presently, two main types of datasets are used in this research field: scripted and unscripted. Scripted datasets feature staged performances by actors depicting emotions, while unscripted datasets capture natural reactions, though acquiring them poses challenges and requires collaboration with experts. Convolutional Neural Networks (CNNs) have been favored for stress classification, but they require substantial data points per class. Alternatively, traditional machine learning classifiers have shown promising with smaller datasets, though their accuracy rates often fall short. This study fused two scripted datasets, RAVDESS and TESS, to enhance stress classification. Utilizing Mel-frequency Cepstral Coefficients (MFCCs) alongside CNNs proved vital in highlighting stress attributes for effective classification with 94.5% accuracy and surpassed the previous studies.

Item Type:	Proceeding Paper (Slide Presentation)
Uncontrolled Keywords:	Stress Classification, RAVDESS, TESS, Speech, MFCCs, CNN
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	DR. Ani Liza Asnawi
Date Deposited:	20 Sep 2024 09:41
Last Modified:	20 Sep 2024 10:43
URI:	http://irep.iium.edu.my/id/eprint/114504

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