Interactivity, Game Creation, Design, Learning, and Innovation. 6th International Conference, ArtsIT 2017, and Second International Conference, DLI 2017, Heraklion, Crete, Greece, October 30–31, 2017, Proceedings

Research Article

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

  • @INPROCEEDINGS{10.1007/978-3-319-76908-0_37,
        author={Alexandros Roniotis and Manolis Tsiknakis},
        title={Detecting Depression Using Voice Signal Extracted by Chatbots: A Feasibility Study},
        proceedings={Interactivity, Game Creation, Design, Learning, and Innovation. 6th International Conference, ArtsIT 2017, and Second International Conference, DLI 2017, Heraklion, Crete, Greece, October 30--31, 2017, Proceedings},
        proceedings_a={ARTSIT 2017 AND DLI},
        year={2018},
        month={3},
        keywords={Virtual coach Cancer Detecting depression Machine learning MFCCs},
        doi={10.1007/978-3-319-76908-0_37}
    }
    
  • Alexandros Roniotis
    Manolis Tsiknakis
    Year: 2018
    Detecting Depression Using Voice Signal Extracted by Chatbots: A Feasibility Study
    ARTSIT 2017 AND DLI
    Springer
    DOI: 10.1007/978-3-319-76908-0_37
Alexandros Roniotis1,*, Manolis Tsiknakis1,*
  • 1: Technological Educational Institute of Crete
*Contact email: alexandros.roniotis@gmail.com, tsiknaki@staff.teicrete.gr

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.