Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Detecting Postpartum Depression in Depressed People by Speech Features

  • Conference paper
  • First Online:

Abstract

Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Pitt, B.: ‘Atypical’ depression following childbirth. Br. J. Psychiatry114(516), 1325–1335 (1968)

    Article  Google Scholar 

  2. Burke, L.: The impact of maternal depression on familial relationships. Int. Rev. Psychiatry15(3), 243–255 (2003)

    Article  Google Scholar 

  3. American College of Obstetricians and Gynecologists. Committee on Obstetric Practice, Committee opinion no. 453: Screening for depression during and after pregnancy. Obstet. Gynecol.115(2 Pt 1), 394–395 (2010)

    Google Scholar 

  4. Accounts Payable Association: Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Publishing (2013)

    Google Scholar 

  5. Kramer, E.: Elimination of verbal cues in judgments of emotion from voice. J. Abnorm. Soc. Psychol.68(4), 390–396 (1964)

    Article  Google Scholar 

  6. Cannizzaro, M., Harel, B., Reilly, N., Chappell, P., Snyder, P.J.: Voice acoustical measurement of the severity of major depression. Brain Cognit.56(1), 30–35 (2004)

    Article  Google Scholar 

  7. Sobin, C., Sackeim, H.A.: Psychomotor symptoms of depression. Am. J. Psychiatry154(1), 4–17 (1997)

    Article  Google Scholar 

  8. Nwe, T.L., Foo, S.W., De Silva, L.C.: Speech emotion recognition using hidden Markov models. Speech Commun.41(4), 603–623 (2003)

    Article  Google Scholar 

  9. Wu, S., Falk, T.H., Chan, W.-Y.: Automatic speech emotion recognition using modulation spectral features. Speech Commun.53(5), 768–785 (2011)

    Article  Google Scholar 

  10. Ellgring, H., Scherer, P.K.R.: Vocal indicators of mood change in depression. J. Nonverbal Behav.20(2), 83–110 (1996)

    Article  Google Scholar 

  11. Mundt, J.C., Vogel, A.P., Feltner, D.E., Lenderking, W.R.: Vocal acoustic biomarkers of depression severity and treatment response. Biol. Psychiatry72(7), 580–587 (2012)

    Article  Google Scholar 

  12. Flint, A.J., Black, S.E., Campbell-Taylor, I., Gailey, G.F., Levinton, C.: Abnormal speech articulation, psychomotor retardation, and subcortical dysfunction in major depression. J. Psychiatr. Res.27(3), 309–319 (1993)

    Article  Google Scholar 

  13. Mandal, M.K., Srivastava, P., Singh, S.K.: Paralinguistic characteristics of speech in schizophrenics and depressives. J. Psychiatr. Res.24(2), 191–196 (1990)

    Article  Google Scholar 

  14. Porritt, L.L., Zinser, M.C., Bachorowski, J.-A., Kaplan, P.S.: Depression diagnoses and fundamental frequency-based acoustic cues in maternal infant-directed speech. Lang. Learn. Dev.10(1), 51–67 (2014)

    Article  Google Scholar 

  15. Cohen, A.S., Kim, Y., Najolia, G.M.: Psychiatric symptom versus neurocognitive correlates of diminished expressivity in schizophrenia and mood disorders. Schizophr. Res.146(1–3), 249–253 (2013)

    Article  Google Scholar 

  16. Tolkmitt, F., Helfrich, H., Standke, R., Scherer, K.R.: Vocal indicators of psychiatric treatment effects in depressives and schizophrenics. J. Commun. Disord.15(3), 209–222 (1982)

    Article  Google Scholar 

  17. Cohen, A.S., Elvevåg, B.: Automated computerized analysis of speech in psychiatric disorders. Curr. Opin. Psychiatry27(3), 203–209 (2014)

    Article  Google Scholar 

  18. Cummins, N., Joshi, J., Dhall, A., Sethu, V., Goecke, R., Epps, J.: Diagnosis of depression by behavioural signals: a multimodal approach. In: Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, New York, NY, USA, pp. 11–20 (2013)

    Google Scholar 

  19. Cohn, J.F., et al.: Detecting depression from facial actions and vocal prosody. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–7 (2009)

    Google Scholar 

  20. Yang, F., et al.: Clinical features of and risk factors for major depression with history of postpartum episodes in Han Chinese women: a retrospective study. J. Affect. Disord.183, 339–346 (2015)

    Article  Google Scholar 

  21. Eyben, F., Weninger, F., Gross, F., Schuller, B.: Recent developments in openSMILE, the Munich open-source multimedia feature extractor. In: Proceedings of the 21st ACM International Conference on Multimedia, New York, NY, USA, pp. 835–838 (2013)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Basic Research Program of China (973 Program) (No. 2014CB744603), and Natural Science Foundation of Hubei Province (2016CFB208).

Author information

Authors and Affiliations

  1. Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

    Jingying Wang, Xiaoyun Sui, Yang Zhou & Tingshao Zhu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Jingying Wang, Yuanbo Gao & Yang Zhou

  3. School of Information Science and Engineering, Lanzhou University, Gansu, 730000, China

    Bin Hu

  4. Department of Psychiatry and Biobehavioral Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, 90095, USA

    Jonathan Flint

  5. School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China

    Shuotian Bai

Authors
  1. Jingying Wang

    You can also search for this author inPubMed Google Scholar

  2. Xiaoyun Sui

    You can also search for this author inPubMed Google Scholar

  3. Bin Hu

    You can also search for this author inPubMed Google Scholar

  4. Jonathan Flint

    You can also search for this author inPubMed Google Scholar

  5. Shuotian Bai

    You can also search for this author inPubMed Google Scholar

  6. Yuanbo Gao

    You can also search for this author inPubMed Google Scholar

  7. Yang Zhou

    You can also search for this author inPubMed Google Scholar

  8. Tingshao Zhu

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence toJonathan Flint orTingshao Zhu.

Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe Ltd., Hayes, United Kingdom

    Bo Hu

Rights and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, J.et al. (2018). Detecting Postpartum Depression in Depressed People by Speech Features. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_46

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

[8]ページ先頭
©2009-2025 Movatter.jp