Abstract
In the whole world especially developed countries, increasing mental health disorders is a serious problem. As a countermeasure, the main objective of this paper is an attempt to estimate depressive status from voice. In this study, we gathered patients with major depressive disorders in the hospital’s consulting room. Several questionnaires including “the Hamilton Depression Rating Scale” (HAM-D) were administered to evaluate the patients’ depressed state. Voices corresponding to three long vowels were recorded from the subjects. Next, the acoustic feature quantity was calculated based on the voice. We developed the HAM-D score estimation algorithm from the voice using one of three types of long vowel audio content. As a result, there was a correlation between the “Actual HAM-D Score” and the “Estimated HAM-D Score”. We found that the algorithm is effective in estimating depression state and can be used for estimating the disease state based on voice.
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References
Hamilton, M.: Rating depressive patients. J. Clin. Psychiatry 41, 21–24 (1980)
Kroenke, K., Spitzer, R.L., Williams, J.B.: The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 2001(16), 606–613 (2001)
Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. Arch. Gen. Psychiatry 4, 561–571 (1961)
Beck, A.T., Steer, R.A., Carbin, M.G.: Psychometric properties of the Beck Depression Inventory twenty-five years of evaluation. Clin. Psychol. Rev. 8, 77–100 (1988)
Izawa, S., et al.: Salivary dehydroepiandrosterone secretion in response to acute psychosocial stress and its correlations with biological and psychological changes. Biol. Psychol. 79(3), 294–298 (2008)
Ito, Y., et al.: Relationships between salivary melatonin levels, quality of sleep, and stress in young Japanese females. Int. J. Tryptophan Res. 6(Suppl. 1), 75–85 (2013)
Sekiyama, A.: Interleukin-18 is involved in alteration of hipothalamic-pituitary-adrenal axis activity by stress. In: Society of Biological Psychiatry Annual Meeting, San Diego, USA (2007)
Kawamura, N., Shinoda, K., Ohashi, Y., Ishikawa, T., Sato, H.: Biomarker for depression, method for measuring a biomarker for depression, computer program, and recording medium. U. S. Patent, US2015126623 (2015)
Hagiwara, N., et al.: Validity of mind monitoring system as a mental health indicator using voice. Adv. Sci. Technol. Eng. Syst. J. 2(3), 338–344 (2017)
Tokuno, S.: Pathophysiological voice analysis for diagnosis and monitoring of depression. In: Kim, Y.-K. (ed.) Understanding Depression, pp. 83–95. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6577-4_6
Yang, Y., Fairbairn, C., Cohn, J.F.: Detecting depression severity from vocal prosody. IEEE Trans. Affect. Comput. 4(2), 142–150 (2013)
Sheehan, D.V., et al.: The Mini-International Neuropsychiatric Interview (M.I.N.I): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59(Suppl. 20), 22–33 (1998)
Eyben, F., Wöllmer, M., Schuller, B.: Opensmile: the munich versatile and fast open-source audio feature extractor. In: Bimbo, A.D., Chang, S.F., Smeulders, A.W.M. (eds.) ACM Multimedia, pp. 1459–1462 (2010)
Hall, M., et al.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Netw. 11, 1188–1193 (1999)
Zimmerman, M., Martinez, J.H., Young, D., Chelminski, I., Dalrymple, K.: Severity classification on the Hamilton depression rating scale. J. Affect. Disord. 150(2), 384–388 (2013)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Omiya, Y. et al. (2019). An Attempt to Estimate Depressive Status from Voice. In: Cipresso, P., Serino, S., Villani, D. (eds) Pervasive Computing Paradigms for Mental Health. MindCare 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-25872-6_13
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DOI: https://doi.org/10.1007/978-3-030-25872-6_13
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