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2018 | OriginalPaper | Chapter

Detecting Depression Using Voice Signal Extracted by Chatbots: A Feasibility Study

Authors : Alexandros Roniotis, Manolis Tsiknakis

Published in: Interactivity, Game Creation, Design, Learning, and Innovation

Publisher: Springer International Publishing

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Abstract

This work aims at proposing a novel framework for detecting depression, like commonly met in cancer patients, using prosodic and statistical features extracted by voice signal. This work presents the first results of extracting these features on test and training sets extracted from the AVEC2016 dataset using MATLAB. The results indicate that voice can be used for extracting depression indicators and developing a mobile application for integrating this new knowledge could be the next step.

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World Health Organization, World Cancer Report 2014 (2014)

Dalton, S., Laursen, T., Mortensen, P., Johansen, C.: Risk for hospitalization with depression after a cancer diagnosis: a nationwide, population-based study of cancer patients in Denmark from 1973 to 2003. J. Clin. Oncol. 27(9), 1440–1445 (2009)CrossRef

Greek National Research Institute, Mental Health - Contemporary Approaches and Reflections, Athens (2011)

Moussas, G., Papadopoulos, A., Christodoulaki, A., Karkanias, A.: Psychological and psychiatric problems in patients with cancer: relationship with the localization of the disease. Psychiatry 23(1), 46–60 (2012)

American Psychiatric Association, Diagnostic and statistical manual of mental disorders, Fourth Edition, Text Revision: DSM-IV-TR. American Psychiatric Publishing Inc., Washington, DC (2000)

Karapoulios, D., Getsios, I., Rizou, V., Tsiklitara, A., Kostopoulou, S., Balodimou, Ch., Margari, N.: Anxiety and depression in patients with lung cancer under chemotherapy. Evaluation with the hospital anxiety and depression scale HADS. In: Asclepios Step, pp. 428–440, Athens (2013)

Mathers, C., Boerma, T., Ma Fat, D.: The Global Burden of Disease: 2004 Update. WHO, Geneva (2008)

American Cancer Society, Depression Increases Cancer Patients’ Risk of Dying (2009)

Fotiadou, A., Priftis, F., Kiprianos, S.: The role of primary health care in the treatment of people with mental disorder. Brain 41(1) (2004)

10.

Cesar, J., Chavoushi, F.: Depression, WHO - Priority Medicines for Europe and the World (2013 Update) (2013)

11.

Kampakis, S., Tsironis, Th.: The role of engineering Learning in Clinical Psychiatry - Application on depressed patients data, Thessaloniki (2011)

12.

Gay, V., Leijdekkers, P.: A health monitoring system using smart phones and wearable sensors. Int. J. ARM 8, 29–35 (2007)

13.

van Wissen, A., Vinkers, C., van Halteren, A.: Developing a Virtual coach for chronic patients: a user study on the impact of similarity, familiarity and realism. In: Meschtscherjakov, A., De Ruyter, B., Fuchsberger, V., Murer, M., Tscheligi, M. (eds.) PERSUASIVE 2016. LNCS, vol. 9638, pp. 263–275. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31510-2_23

14.

Ellis, T., Latham, N., DeAngelis, T., Thomas, C., Saint-Hilaire, M., Bickmore, T.: Feasibility of a virtual exercise coach to promote walking in community-dwelling persons with parkinson disease. Am. J. Phys. Med. Rehabil. 92(6), 472–485 (2013)CrossRef

15.

Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P., Patel, V., Haines, A.: The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med. 10, e1001363 (2013)CrossRef

16.

Albrecht, T., Herrick, C.: 100 Questions and Answers About Depression, p. 212. Jones & Bartlett Publishers, Sudbury (2010)

17.

Sahu, S., Espy-Wilson, C.: Effect of depression on syllabic rate of speech. J. Acoust. Soc. Am. 138, 1781 (2015)CrossRef

18.

Ozdas, A., Shiavi, R., Silverman, S., Wilkes, D.: Analysis of fundamental frequency for near term suicidal risk assessment. In: 2000 IEEE International Conference in Systems, Man, and Cybernetics (2000)

19.

Hamilton, H.: A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56–62 (1960)CrossRef

20.

Beck, A., Steer, R., Brown, G.: Beck Depression Inventory-II. The Psychological Corporation, (1961–1996)

21.

Kroenke, K., Spitzer, R., Williams, J.: The PHQ-9, validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001)CrossRef

22.

Roy, N., Nissen, S., Sapir, S.: Articulatory changes in muscle tension dysphonia: evidence of vowel space expansion following manual circumlaryngeal therapy. J. Commun. Disord. 42(2), 124–135 (2009)CrossRef

23.

Kreibig, S.: Autonomic nervous system activity in emotion: a review. Biol. Psychol. 84(3), 394–421 (2010)CrossRef

24.

Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., Quatieri, T.: A review of depression and suicide risk assessment using speech analysis. Speech Commun. 71, 10–49 (2015)CrossRef

25.

Pampouchidou, A., Simantiraki, O., Fazlollahi, A., Manousos, D., Pediaditis, M., Roniotis, A., Giannakakis, G., Meriaudeau, F., Simos, P., Marias, K., Yang, F., Tsiknakis, M.: Depression assessment by fusing high and low level features from audio, video, and text. In: 6th International Workshop on Audio/Visual Emotion Challenge, Amsterdam, Netherlands, pp. 27–34 (2016)

26.

France, D., Shiavi, R., Silverman, S., Silverman, M., Wilkes, M.: Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans. Biomed. Eng. 47(7), 829–837 (2000)CrossRef

27.

Laosaphan, T., Yingthawornsuk, T.: Classification of depressed speakers based on MFCC in speech samples. In: ICAEEE 2012, Pattaya, Thailand (2012)

28.

Sturim, D., Torres-Carrasquillo, P., Quatieri, T., Malyska, N., McCree, A.: Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis. In: Proceedings of Interspeech (2011)

29.

Yuan, M.: Chatbots: Building Intelligent Bots. Addison-Wesley, New York (2016)

30.

Greene, J., Hibbard, J., Sacks, R., Overton, V., Parrotta, C.: When patient activation levels change, health outcomes and costs change, too. Health Aff. 34(3), 431–437 (2015)CrossRef

31.

Bloss, C., Wineinnger, N., Peters, M., Boeldt, D., Ariniello, L., Kim, J.Y., Sheard, J., Komatireddy, R., Barrett, P., Topol, E.: A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors. PeerJ 4, e1554 (2016)CrossRef

32.

Freeney, D.: Usability Versus Persuasion in an Application Interface Design. Institute for Innovation Design & Engineering, Mälardalen University, Eskilstuna, Sweden (2014)

33.

The Nielsen Company, So Many Apps, So Much More Time for Entertainment (2016)

34.

Zillman, M.P.: Healthcare Bots and Subject Directories (2016)

35.

Bots, the next frontier, The Economist (2016)

36.

Versluis, A., Verkuil, B., Spinhoven, P., van der Ploeg, M., Brosschot, J.: Changing mental health and positive psychological well-being using ecological momentary interventions: a systematic review and meta-analysis. J. Med. Internet Res. 18(6), e152 (2016)CrossRef

37.

Proyer, R., Gander, F., Wellenzohn, S., Willibald, R.: Positive psychology interventions in people aged 50–79 years: long-term effects of placebocontrolled online interventions on well-being and depression. Aging Mental Health 18(8), 997–1005 (2014)CrossRef

38.

Sahidullah, M., Goutam, S.: Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition. Speech Commun. 54(4), 543–565 (2012)CrossRef

39.

Dasarathy, B.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques (1991)

Title: Detecting Depression Using Voice Signal Extracted by Chatbots: A Feasibility Study
Authors: Alexandros Roniotis
Manolis Tsiknakis
Publisher: Springer International Publishing
Book: Interactivity, Game Creation, Design, Learning, and Innovation
Print ISBN: 978-3-319-76907-3

Electronic ISBN: 978-3-319-76908-0

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-76908-0_37

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