Classification of stress based on speech features

Contemporary life is filled with challenges, hassles, deadlines, disappointments, and endless demands. The consequent of which might be stress. Stress has become a global phenomenon that is been experienced in our modern daily lives. Stress might play a significant role in psychological and/or beh...

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Main Author:	Jasim, Arshed Ahmed
Format:	Thesis
Language:	eng eng
Published:	2014
Subjects:	QA76 Computer software
Online Access:	https://etd.uum.edu.my/4372/1/s812886.pdf https://etd.uum.edu.my/4372/7/s812886_abstract.pdf
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id	my-uum-etd.4372
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mohd. Yusof, Shahrul Azmi Mohamed Din, Aniza
topic	QA76 Computer software
spellingShingle	QA76 Computer software Jasim, Arshed Ahmed Classification of stress based on speech features
description	Contemporary life is filled with challenges, hassles, deadlines, disappointments, and endless demands. The consequent of which might be stress. Stress has become a global phenomenon that is been experienced in our modern daily lives. Stress might play a significant role in psychological and/or behavioural disorders like anxiety or depression. Hence early detection of the signs and symptoms of stress is an antidote towards reducing its harmful effects and high cost of stress management efforts. This research work thereby presented Automatic Speech Recognition (ASR) technique to stress detection as a better alternative to other approaches such as chemical analysis, skin conductance, electrocardiograms that are obtrusive, intrusive, and also costly. Two set of voice data was recorded from ten Arabs students at Universiti Utara Malaysia (UUM) in neural and stressed mode. Speech features of fundamental, frequency (f0); formants (F1, F2, and F3), energy and Mel-Frequency Cepstral Coefficients (MFCC) were extracted and classified by K-nearest neighbour, Linear Discriminant Analysis and Artificial Neural Network. Result from average value of fundamental frequency reveals that stress is highly correlated with increase in fundamental frequency value. Of the three classifiers, K-nearest neighbor (KNN) performance is best followed by linear discriminant analysis (LDA) while artificial neural network (ANN) shows the least performance. Stress level classification into low, medium and high was done based of the classification result of KNN. This research shows the viability of ASR as better means of stress detection and classification.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Jasim, Arshed Ahmed
author_facet	Jasim, Arshed Ahmed
author_sort	Jasim, Arshed Ahmed
title	Classification of stress based on speech features
title_short	Classification of stress based on speech features
title_full	Classification of stress based on speech features
title_fullStr	Classification of stress based on speech features
title_full_unstemmed	Classification of stress based on speech features
title_sort	classification of stress based on speech features
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2014
url	https://etd.uum.edu.my/4372/1/s812886.pdf https://etd.uum.edu.my/4372/7/s812886_abstract.pdf
_version_	1747827725256622080
spelling	my-uum-etd.43722022-05-23T01:52:30Z Classification of stress based on speech features 2014 Jasim, Arshed Ahmed Mohd. Yusof, Shahrul Azmi Mohamed Din, Aniza Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA76 Computer software Contemporary life is filled with challenges, hassles, deadlines, disappointments, and endless demands. The consequent of which might be stress. Stress has become a global phenomenon that is been experienced in our modern daily lives. Stress might play a significant role in psychological and/or behavioural disorders like anxiety or depression. Hence early detection of the signs and symptoms of stress is an antidote towards reducing its harmful effects and high cost of stress management efforts. This research work thereby presented Automatic Speech Recognition (ASR) technique to stress detection as a better alternative to other approaches such as chemical analysis, skin conductance, electrocardiograms that are obtrusive, intrusive, and also costly. Two set of voice data was recorded from ten Arabs students at Universiti Utara Malaysia (UUM) in neural and stressed mode. Speech features of fundamental, frequency (f0); formants (F1, F2, and F3), energy and Mel-Frequency Cepstral Coefficients (MFCC) were extracted and classified by K-nearest neighbour, Linear Discriminant Analysis and Artificial Neural Network. Result from average value of fundamental frequency reveals that stress is highly correlated with increase in fundamental frequency value. Of the three classifiers, K-nearest neighbor (KNN) performance is best followed by linear discriminant analysis (LDA) while artificial neural network (ANN) shows the least performance. Stress level classification into low, medium and high was done based of the classification result of KNN. This research shows the viability of ASR as better means of stress detection and classification. 2014 Thesis https://etd.uum.edu.my/4372/ https://etd.uum.edu.my/4372/1/s812886.pdf text eng public https://etd.uum.edu.my/4372/7/s812886_abstract.pdf text eng public other masters Universiti Utara Malaysia Abu Shariah, M., Ainon, R. N., Zainuddin, R., & Khalifa, O.O. (2007) Human computer interaction using isolated-words speech recognition technology. Paper presented at the Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on. Acero, A.(1990). Acoustical and environmental robustness in automatic speech recognition (Doctoral dissertation, Carnegie Mellon University). Adami, A. G., Lazzarotto, G. B., Foppa, E. F., & Couto Barone, D. (1999). 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Classification of stress based on speech features

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