Depression Detection Through Speech Analysis : A Survey

Authors

  • Aastik Malviya  Department of Computer Science and Engineering, SVKM's NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India
  • Rahul Meharkure  Department of Computer Science and Engineering, SVKM's NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India
  • Rohan Narsinghani  Department of Computer Science and Engineering, SVKM's NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India
  • Viraj Sheth  Department of Computer Science and Engineering, SVKM's NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India
  • Pratiksha Meshram  Department of Computer Science and Engineering, SVKM's NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT1952190

Keywords:

Teager Energy Operator (TEO), Hamilton Depression Rating Scale (HAM-D), Major Depression Disorder (MDD), Support Vector Machine (SVM), Low-Level Descriptors (LLD), Gaussian Mixture Model (GMM), minimalredundancy-maximal relevance (mRMR), Sequential Forward Floating Selection (SFFS), Principal Component Analysis (PCA).

Abstract

Depression is a common and serious medical illness which affects the way how we think, feel and act. Although harmless in its initial stages, it can cause serious problems if detected at a later stage. Due to advancements in technology, it is now possible to detect signs of depression. Different implementation of machine learning algorithms has been worked upon to detect factors causing depression. It is found that speech of a person is dramatically affected and various vocal features are used to classify depression.

References

  1. Enrique Garcia-Cejaa et al. "Mental health monitoring with multi modal sensing and machine learning: A survey" in Elsevier, 2018.
  2. Amanda Cibelly Brito Gois et al. "Factors associated with voice disorders among the elderly: a schematic review" in Brazilian Journal of Otorhinolaryngology, 2017.
  3. Blessing Ojeme et al. "Selecting Learning Algorithms for Simultaneous Identification of Depression and Comorbid Disorders" at 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, 2016.
  4. Asli Ozdas et al. "Investigation of Vocal Jitter and Glottal Flow Spectrum as Possible Cues for Depression and Near-Term Suicidal Risk" at IEEE Transactions on Biomedical Engineering, Vol. 51, No. 9,2004.
  5. Lu-Shih Alex Low "Detection of Clinical Depression in Adolescents’ Speech During Family Interactions" at IEEE Transactions on Biomedical Engineering, Vol. 58, No. 3,2011.
  6. Rachelle Horwitz et al. "On the Relative Importance of Vocal Source, System and Prosody in Human Depression" at 2013 IEEE International Conference on Body Sensor Networks, 2013.
  7. Sharifa Alghowinem "Detecting Depression: A Comparison Between Spontaneous and Read speech" at IEEE ICASSP, 2013.
  8. Andrea Guidi "Voice Quality in patients suffering from Bipolar Disease" at 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.
  9. Andrea Guidi et al. "Analysis of running speech for the characterization of mood state in bipolar patient" at AEIT International Annual Conference, 2015.
  10. A. Pampouchidou et al. "Facial Geometry and Speech Analysis for Depression Detection" at 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017.
  11. Jingying Wang "Identifying Comorbidities from Depressed People via Voice Analysis" at IEEE Conference on BIBM, 2017.
  12. Yasuhiro Omiya "Difference in Speech Analysis Results by Compression" at IEEE ICIIBMS, 2017.
  13. Miss. Pallavi R.Parekh et al. "Clinical Depression Detection for Adolescent by Speech Features" at ICECDS, 2017.
  14. Yalin Li et al. "EEG-based Mild Depressive Detection Using Differential Evolution" at IEEEAccess Vol. 7, 2018.
  15. Hailiang Long et al. "Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram" at Journal of Health and Medical Informatics, 2017.
  16. Nicholas Cummins et al. "An Investigation of Depressed Speech Detection: Features and Normalization" at 12th Annual Conference of the International Speech Communication Association, Florence, Italy, August 27-31, 2011.
  17. Shweta Oak "Depression Detection and Analysis" at The AAAI Spring Symposium on Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing Technical Report, 2017.
  18. A. Pampouchidou et al. "Facial Geometry and Speech Analysis for Depression Detection" at 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'17) , Jul 2017, Jeju Island, South Korea. IEEE, 2017.
  19. Zhenyu Liu, Bin Hu, Lihua Yan, Tianyang Wang, Fei Liu, Xiaoyu Li, Huanyu Kang "Detection of Depression in Speech" at 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), 2015.
  20. Shamla Mantri, Dr. Pankaj Agrawal, Dr. S.S.Dorle, Dipti Patil, Dr. V.M.Wadhai "Clinical Depression analysis Using Speech Features" at 6th International Conference on Emerging Trends in Engineering and Technology, 2013.
  21. "Voice Analysis Aids Fight Against Suicide Depression" at https://www.cio.com.au/article/648076/voice-analysis-aid-fight-against-suicide-depression/
  22. David Nield "Computer Program Tells When Someone Is Depressed by Speech Patterns" at Sciencealert, 2016.
  23. Rob Matheson "Neural Network Model to Detect Depression in Conversations" at MIT News Office.
  24. Prathamesh Raut et al. "Depression Detection Using BDI And Speech Recognition" at IJRASET, 2018.
  25. Haihua Jiang et al. "Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features" at Computational and Mathematical Methods in Medicine Vol. 2018, 2018.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Aastik Malviya, Rahul Meharkure, Rohan Narsinghani, Viraj Sheth, Pratiksha Meshram, " Depression Detection Through Speech Analysis : A Survey, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.712-716, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952190