Object: This review evaluates the use of smartphone-based voice data for predicting and monitoring depression.
Methods: A scoping review was conducted, examining 14 studies from Medline, Scopus, and Web of Science (2010-2023) on voice data collection methods and the use of voice features for minitoring depression.
Results: Voice data, especially prosodic features like fundamental frequency and pitch, show promise for predicting depression, though their sole predictive power requires further validation. Integrating voice with multimodal sensor data has been shown to improve accuracy significantly.
Conclusion: Smartphone-based voice monitoring offers a promising, noninvasive, and cost-effective approach to depression management. The integration of machine learning with sensor data could significantly enhance mental health monitoring, necessitating further research and longitudinal studies for validation.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11179519 | PMC |
| http://dx.doi.org/10.1177/20552076241261920 | DOI Listing |
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