Integration of MFCCS and CNN for multi-class stress speech classification on unscripted dataset

Zainal, Nur Aishah and Asnawi, Ani Liza and Jusoh, Ahmad Zamani and Ibrahim, Siti Noorjannah and Mohd. Ramli, Huda Adibah (2024) Integration of MFCCS and CNN for multi-class stress speech classification on unscripted dataset. IIUM Egineering Journal, 25 (2). pp. 381-395. ISSN 1511-788X E-ISSN 2289-7860

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Abstract

Stress is an interaction between individuals and their environment, where perceived threats can lead to serious consequences if prolonged and consistently linked to adverse physical and mental health outcomes. Our study explores methods for stress classification via speech, utilizing an unscripted dataset from an experimental study that was able to show the spontaneous reactions of stressed individuals. Mel-Frequency Cepstral Coefficients (MFCCs) emerge as promising speech features, adept at representing the power spectrum crucial to human auditory perception, especially in stress speech recognition. Leveraging deep learning technology, specifically Convolutional Neural Network (CNN), our research optimally combines speech features and CNN algorithms for stress classification. Despite the scarcity of publications on unscripted datasets and multi-class stress classifications, our study advocates their adoption, aiming to enhance performance metrics and contribute to research expansion. The proposed system shows that MFCCs achieve an accuracy of 95.67% in distinguishing among three stress classes (low-stress, medium-stress, and high-stress), surpassing the prior unscripted dataset study by 81.86%. This highlights the efficacy of the proposed MFCCs-CNN system in stress classification.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Multi-class stress classification; Unscripted dataset; Speech stress detection; 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 Kulliyyah of Engineering
Depositing User:	DR. Ani Liza Asnawi
Date Deposited:	19 Sep 2024 09:21
Last Modified:	19 Sep 2024 09:25
URI:	http://irep.iium.edu.my/id/eprint/114502

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