This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (481) Innovations in Mental Health Systems (390) Depression and Mood Disorders; Suicide Prevention (1363) Usability of Apps and User Perceptions of mHealth (974) Diagnostic Tools in Mental Health (242) Formative Evaluation of Digital Health Interventions (2410) Speech and Phonetics in Verbal Protocols (Think Aloud, Recall, and Explanation Tasks) (93)

Published on in Vol 6, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37061, first published .
Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Authors of this article:

Peter Tonn1 Author Orcid Image ;   Lea Seule1 Author Orcid Image ;   Yoav Degani2 Author Orcid Image ;   Shani Herzinger2 Author Orcid Image ;   Amit Klein2 Author Orcid Image ;   Nina Schulze1 Author Orcid Image
Article Authors Cited by (1) Tweetations (5) Metrics

Journals

  1. Wasserzug Y, Degani Y, Bar-Shaked M, Binyamin M, Klein A, Hershko S, Levkovitch Y. Development and validation of a machine learning-based vocal predictive model for major depressive disorder. Journal of Affective Disorders 2023;325:627 View