Automated speech-based screening of depression using deep convolutional neural networks

Author:

Chlasta Karol,Wołk Krzysztof,Krejtz Izabela

Publisher

Elsevier BV

Subject

General Engineering

Reference28 articles.

1. World Health Organization (2017) "WHO global health days - Staying positive and preventing depression as you get older." Retrieved from https://www.who.int/campaigns/world-health-day/2017/handouts-depression/older-age/en/

2. World Health Organization. (2018) “Agenda Item 12.4 Digital Health.” In Proceedings of Seventy-First World Health Assembly, 21–26 May 2018 in Geneva, Switzerland. (pp. 2-3). http://apps.who.int/gb/ebwha/pdf_files/WHA71/A71_R7-en.pdf

3. Quatieri, Thomas F., and Nicolas Malyska (2012) “Vocal-source biomarkers for depression: A link to psychomotor activity.” In Thirteenth Annual Conference of the International Speech Communication Association.

4. "A review of depression and suicide risk assessment using speech analysis.”;Cummins;Speech Communication,2015

5. Ringeval, Fabien, Björn Schuller, Michel Valstar, Jonathan Gratch, Roddy Cowie, Stefan Scherer, Sharon Mozgai, Nicholas Cummins, Maximilian Schmitt, and Maja Pantic (2017, October) “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.

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