Detecting Depression from Voice

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. Dham, S., Sharma, A., Dhall, A.: Depression scale recognition from audio, visual and text analysis. arXiv preprint arXiv:1709.05865 (2017)

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. 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. He, L., Cao, C.: Automated depression analysis using convolutional neuralnetworks from speech. J. Biomed. Inform. 83, 103–111 (2018)

   Article  Google Scholar 

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. 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)

   Article  Google Scholar 

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)

   Article  Google Scholar 

9. Morales, M.R.: Multimodal depression detection: an investigation of features and fusion techniques for automated systems (2018)

   Google Scholar 

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. 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. 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. 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. 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. 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. 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. 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. 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 

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