Integration of MFCCS and CNN for multi-class stress speech classification onunscripted dataset - Zainal, Nur Aishah; Asnawi, Ani Liza; Jusoh, Ahmad Zamani; Ibrahim, Siti Noorjannah; Mohd. Ramli, Huda Adibah (2024)
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.