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

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Interactivity, Game Creation, Design, Learning, and Innovation (ArtsIT 2017, DLI 2017)

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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Acknowledgments

This research is supported by IKY scholarships programme and co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the action ‘‘Reinforcement of Postdoctoral Researchers” in the framework of the Operational Programme ‘‘Human Resources Development Program, Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) 2014–2020.

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Authors and Affiliations

  1. Department of Informatics Engineering, Technological Educational Institute of Crete, Heraklion, Greece

    Alexandros Roniotis & Manolis Tsiknakis

Authors
  1. Alexandros Roniotis
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  2. Manolis Tsiknakis
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Corresponding author

Correspondence to Alexandros Roniotis .

Editor information

Editors and Affiliations

  1. Aalborg University, Esbjerg, Denmark

    Anthony L. Brooks

  2. Aalborg University, Aalborg, Denmark

    Eva Brooks

  3. Technological Educational Institute of Crete, Heraklion, Greece

    Nikolas Vidakis

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Roniotis, A., Tsiknakis, M. (2018). Detecting Depression Using Voice Signal Extracted by Chatbots: A Feasibility Study. In: Brooks, A., Brooks, E., Vidakis, N. (eds) Interactivity, Game Creation, Design, Learning, and Innovation. ArtsIT DLI 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-319-76908-0_37

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  • DOI: https://doi.org/10.1007/978-3-319-76908-0_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76907-3

  • Online ISBN: 978-3-319-76908-0

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