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Microphone-Independent Speech Features for Automatic Depression Detection Using Recurrent Neural Network

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Proceedings of the 8th International Conference on Computational Science and Technology

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 835))

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Abstract

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%.

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Acknowledgements

This work was supported by funding from the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS/1/2018/TK04/UIAM/02/7).

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Faculty of Engineering, International Islamic University Malaysia, Gombak, Malaysia

    Mugahed Al-Ezzi Ahmed Ezzi & Nik Nur Wahidah Nik Hashim

  2. Department of Psychiatry, Faculty of Medicine, International Islamic University Malaysia, Jalan Hospital, 25000, Kuantan, Pahang, Malaysia

    Nadzirah Ahmad Basri

Authors
  1. Mugahed Al-Ezzi Ahmed Ezzi
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  2. Nik Nur Wahidah Nik Hashim
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  3. Nadzirah Ahmad Basri
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Corresponding author

Correspondence to Nik Nur Wahidah Nik Hashim .

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Editors and Affiliations

  1. Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

    Rayner Alfred

  2. School of Information, Science, Security, Network, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Yuto Lim

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Ezzi, M.AE.A., Hashim, N.N.W.N., Basri, N.A. (2022). Microphone-Independent Speech Features for Automatic Depression Detection Using Recurrent Neural Network. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_54

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