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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CITATION STYLE
Roniotis, A., & Tsiknakis, M. (2018). Detecting depression using voice signal extracted by chatbots: A feasibility study. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 229, pp. 386–392). Springer Verlag. https://doi.org/10.1007/978-3-319-76908-0_37
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