Microphone-independent speech features for automatic depression detection using recurrent neural network
Depression is a common mental disorder that has a negative impact on individuals, society, and the economy. Traditional clinical diagnosis methods are subjective and necessitate extensive expert participation. Because it is fast, convenient, and non-invasive, automatic depression detection using spe...
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Online Access: | http://irep.iium.edu.my/98933/1/98933_Microphone-independent%20speech%20features.pdf http://irep.iium.edu.my/98933/2/98933_Microphone-independent%20speech%20features_SCOPUS.pdf http://irep.iium.edu.my/98933/ https://link.springer.com/chapter/10.1007/978-981-16-8515-6_54 |
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my.iium.irep.989332022-07-27T02:34:36Z http://irep.iium.edu.my/98933/ Microphone-independent speech features for automatic depression detection using recurrent neural network Nik Hashim, Nik Nur Wahidah Ahmad Basri, Nadzirah Ahmad Ezzi, Mogahed Al Ezzi BF Psychology Q Science (General) Depression is a common mental disorder that has a negative impact on individuals, society, and the economy. Traditional clinical diagnosis methods are subjective and necessitate extensive expert participation. Because it is fast, convenient, and non-invasive, automatic depression detection using speech signals is a promising depression objective biomarker. Acoustic feature extraction is one of the most challenging techniques for speech analysis applications in mobile phones. The values of the extracted acoustic features are significantly influenced by adverse environmental noises, a wide range of microphone specifications, and various types of recording software. This study identified microphone-independent acoustic features and utilized them in developing an end-to-end recurrent neural network model to classify depression from Bahasa Malaysia speech. The dataset includes 110 female participants. Patient Health Questionnaire 9, Malay Beck Depression Inventory-II, and subjects’ declaration of Major Depressive Disorder diagnosis by a trained clinician were used to determine depression status. Multiple combinations of speech types were compared and discussed. Robust acoustic features derived from female spontaneous speech achieved an accuracy of 85%. Springer 2022-03 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/98933/1/98933_Microphone-independent%20speech%20features.pdf application/pdf en http://irep.iium.edu.my/98933/2/98933_Microphone-independent%20speech%20features_SCOPUS.pdf Nik Hashim, Nik Nur Wahidah and Ahmad Basri, Nadzirah and Ahmad Ezzi, Mogahed Al Ezzi (2022) Microphone-independent speech features for automatic depression detection using recurrent neural network. In: 8th International Conference on Computational Science and Technology ICCST 2021, 28-29 August 2021, Online. https://link.springer.com/chapter/10.1007/978-981-16-8515-6_54 10.1007/978-981-16-8515-6_54 |
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BF Psychology Q Science (General) Nik Hashim, Nik Nur Wahidah Ahmad Basri, Nadzirah Ahmad Ezzi, Mogahed Al Ezzi Microphone-independent speech features for automatic depression detection using recurrent neural network |
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Depression is a common mental disorder that has a negative impact on individuals, society, and the economy. Traditional clinical diagnosis methods are subjective and necessitate extensive expert participation. Because it is fast, convenient, and non-invasive, automatic depression detection using speech signals is a promising depression objective biomarker. Acoustic feature extraction is one of the most challenging techniques for speech analysis applications in mobile phones. The values of the extracted acoustic features are significantly influenced by adverse environmental noises, a wide range of microphone specifications, and various types of recording software. This study identified microphone-independent acoustic features and utilized them in developing an end-to-end recurrent neural network model to classify depression from Bahasa Malaysia speech. The dataset includes 110 female participants. Patient Health Questionnaire 9, Malay Beck Depression Inventory-II, and subjects’ declaration of Major Depressive Disorder diagnosis by a trained clinician were used to determine depression status. Multiple combinations of speech types were compared and discussed. Robust acoustic features derived from female spontaneous speech achieved an accuracy of 85%. |
format |
Conference or Workshop Item |
author |
Nik Hashim, Nik Nur Wahidah Ahmad Basri, Nadzirah Ahmad Ezzi, Mogahed Al Ezzi |
author_facet |
Nik Hashim, Nik Nur Wahidah Ahmad Basri, Nadzirah Ahmad Ezzi, Mogahed Al Ezzi |
author_sort |
Nik Hashim, Nik Nur Wahidah |
title |
Microphone-independent speech features for automatic depression detection using recurrent neural network |
title_short |
Microphone-independent speech features for automatic depression detection using recurrent neural network |
title_full |
Microphone-independent speech features for automatic depression detection using recurrent neural network |
title_fullStr |
Microphone-independent speech features for automatic depression detection using recurrent neural network |
title_full_unstemmed |
Microphone-independent speech features for automatic depression detection using recurrent neural network |
title_sort |
microphone-independent speech features for automatic depression detection using recurrent neural network |
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Springer |
publishDate |
2022 |
url |
http://irep.iium.edu.my/98933/1/98933_Microphone-independent%20speech%20features.pdf http://irep.iium.edu.my/98933/2/98933_Microphone-independent%20speech%20features_SCOPUS.pdf http://irep.iium.edu.my/98933/ https://link.springer.com/chapter/10.1007/978-981-16-8515-6_54 |
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