Advertisement

An Attempt to Estimate Depressive Status from Voice

  • Yasuhiro OmiyaEmail author
  • Takeshi Takano
  • Tomotaka Uraguchi
  • Mitsuteru Nakamura
  • Masakazu Higuchi
  • Shuji Shinohara
  • Shunji Mitsuyoshi
  • Mirai So
  • Shinichi Tokuno
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 288)

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.

Keywords

Vocal analysis Depressive status estimation The Hamilton Depression Rating Scale (HAM-D) 

References

  1. 1.
    Hamilton, M.: Rating depressive patients. J. Clin. Psychiatry 41, 21–24 (1980)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. Arch. Gen. Psychiatry 4, 561–571 (1961)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)MathSciNetGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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_6CrossRefGoogle Scholar
  11. 11.
    Yang, Y., Fairbairn, C., Cohn, J.F.: Detecting depression severity from vocal prosody. IEEE Trans. Affect. Comput. 4(2), 142–150 (2013)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Hall, M., et al.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Yasuhiro Omiya
    • 1
    • 2
    Email author
  • Takeshi Takano
    • 1
    • 3
  • Tomotaka Uraguchi
    • 1
  • Mitsuteru Nakamura
    • 2
  • Masakazu Higuchi
    • 2
  • Shuji Shinohara
    • 3
  • Shunji Mitsuyoshi
    • 3
  • Mirai So
    • 4
  • Shinichi Tokuno
    • 2
  1. 1.PST Inc., Industry & Trade Center Building 905YokohamaJapan
  2. 2.Graduate School of MedicineThe University of TokyoTokyoJapan
  3. 3.Graduate School of EngineeringThe University of TokyoTokyoJapan
  4. 4.Ginza Taimei ClinicTokyoJapan

Personalised recommendations