Advertisement

Detecting Depression from Voice

  • Mashrura TasnimEmail author
  • Eleni Stroulia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11489)

Abstract

In this paper, we present our exploration of different machine-learning algorithms for detecting depression by analyzing the acoustic features of a person’s voice. We have conducted our study on benchmark datasets, in order to identify the best framework for the task, in anticipation of deploying it in a future application.

Keywords

Depression Acoustic features Classification Regression 

References

  1. 1.
    Cummins, N., Epps, J., Sethu, V., Breakspear, M., Goecke, R.: Modeling spectral variability for the classification of depressed speech. In: Interspeech, pp. 857–861 (2013)Google Scholar
  2. 2.
    Dham, S., Sharma, A., Dhall, A.: Depression scale recognition from audio, visual and text analysis. arXiv preprint arXiv:1709.05865 (2017)
  3. 3.
    Fraser, K.C., Rudzicz, F., Hirst, G.: Detecting late-life depression in alzheimer’s disease through analysis of speech and language. In: Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology, pp. 1–11 (2016)Google Scholar
  4. 4.
    Gong, Y., Poellabauer, C.: Topic modeling based multi-modal depression detection. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, pp. 69–76. ACM (2017)Google Scholar
  5. 5.
    He, L., Cao, C.: Automated depression analysis using convolutional neuralnetworks from speech. J. Biomed. Inform. 83, 103–111 (2018)CrossRefGoogle Scholar
  6. 6.
    Lopez-Otero, P., Docio-Fernandez, L., Garcia-Mateo, C.: A study of acoustic features for the classification of depressed speech. In: 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1331–1335. IEEE (2014)Google Scholar
  7. 7.
    Low, L.S.A., Maddage, N.C., Lech, M., Sheeber, L.B., Allen, N.B.: Detection of clinical depression in adolescents’ speech during family interactions. IEEE Trans. Biomed. Eng. 58(3), 574–586 (2011)CrossRefGoogle Scholar
  8. 8.
    Moore II, E., Clements, M.A., Peifer, J.W., Weisser, L.: Critical analysis of the impact of glottal features in the classification of clinical depression in speech. IEEE Trans. Biomed. Eng. 55(1), 96–107 (2008)CrossRefGoogle Scholar
  9. 9.
    Morales, M.R.: Multimodal depression detection: an investigation of features and fusion techniques for automated systems (2018)Google Scholar
  10. 10.
    Özkanca, Y., Demiroglu, C., Besirli, A., Celik, S.: Multi-lingual depression-level assessment from conversational speech using acoustic and text features. In: Proceedings of Interspeech 2018, pp. 3398–3402 (2018)Google Scholar
  11. 11.
    Ringeval, F., et al.: AVEC 2017: Real-life depression, and affect recognition workshop and challenge. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, pp. 3–9. ACM (2017)Google Scholar
  12. 12.
    Samareh, A., Jin, Y., Wang, Z., Chang, X., Huang, S.: Predicting depression severity by multi-modal feature engineering and fusion. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)Google Scholar
  13. 13.
    Sanchez, M.H., Vergyri, D., Ferrer, L., Richey, C., Garcia, P., Knoth, B., Jarrold, W.: Using prosodic and spectral features in detecting depression in elderly males. In: Twelfth Annual Conference of the International Speech Communication Association (2011)Google Scholar
  14. 14.
    Sun, B., et al.: A random forest regression method with selected-text feature for depression assessment. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, pp. 61–68. ACM (2017)Google Scholar
  15. 15.
    Valstar, M., et al.: AVEC 2013: the continuous audio/visual emotion and depression recognition challenge. In: Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, pp. 3–10. ACM (2013)Google Scholar
  16. 16.
    Wang, R., et al.: StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3–14. ACM (2014)Google Scholar
  17. 17.
    Williamson, J.R., Quatieri, T.F., Helfer, B.S., Horwitz, R., Yu, B., Mehta, D.D.: Vocal biomarkers of depression based on motor incoordination. In: Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, pp. 41–48. ACM (2013)Google Scholar
  18. 18.
    Yang, L., Sahli, H., Xia, X., Pei, E., Oveneke, M.C., Jiang, D.: Hybrid depression classification and estimation from audio video and text information. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, pp. 45–51. ACM (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

Personalised recommendations