ArticleIEEE journal of biomedical and health informatics2019
Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood.
Article in IEEE journal of biomedical and health informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Speech-Derived Digital Markers of Treatment Response and Improvement Trajectories in Pediatric Anxiety.Research square · 2026Article
- Targeting neurobehavioral substrates of effortful control to reduce childhood anxiety: The Camp Kidpower study protocol.Research square · 2025Article
- Fabla: A voice-based ecological assessment method for securely collecting spoken responses to researcher questions.Behavior research methods · 2025Article
- Preschool Mood Disorders: A Review of the Literature from 2017 to 2024.Child and adolescent psychiatric clinics of North America · 2025Review
- How Anxiety State Influences Speech Parameters: A Network Analysis Study from a Real Stressed Scenario.Brain sciences · 2025Article
- Sentence-level multi-modal feature learning for depression recognition.Frontiers in psychiatry · 2025Article
- Siamese Neural Network for Speech-Based Depression Classification and Severity Assessment.Journal of healthcare informatics research · 2024Article
- A classification system based on improved global exploration and convergence to examine student psychological fitness.Scientific reports · 2024Article
- A Louder Call for the Integration of Multiple Nonverbal Channels in the Study of Affect.Affective science · 2024Article
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges.Sensors (Basel, Switzerland) · 2024Review
- Exploring the impact of type II diabetes mellitus on voice quality.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Exploring a multimodal approach for utilizing digital biomarkers for childhood mental health screening.Frontiers in psychiatry · 2024Article
- The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.IEEE journal of biomedical and health informatics · 2023Article
- The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.medRxiv : the preprint server for health sciences · 2023Article
- A systematic review on automated clinical depression diagnosis.Npj mental health research · 2023Review
- Using Wearable Devices and Speech Data for Personalized Machine Learning in Early Detection of Mental Disorders: Protocol for a Participatory Research Study.JMIR research protocols · 2023Article
- A Wearable Artificial Intelligence Feedback Tool (Wrist Angel) for Treatment and Research of Obsessive Compulsive Disorder: Protocol for a Nonrandomized Pilot Study.JMIR research protocols · 2023Article
- Evaluation of the Michigan Clinical Consultation and Care Program: An Evidence-Based Approach to Perinatal Mental Healthcare.Journal of clinical medicine · 2023Article
- Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.Brain informatics · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying young children with internalizing disorders using a 3-min speech task. We show that machine learning analysis of audio data from the task can be used to identify children with an internalizing disorder with 80% accuracy (54% sensitivity, 93% specificity). The speech features most discriminative of internalizing disorder are analyzed in detail, showing that affected children exhibit especially low-pitch voices, with repeatable speech inflections and content, and high-pitched response to surprising stimuli relative to controls. This new tool is shown to outperform clinical thresholds on parent-reported child symptoms, which identify children with an internalizing disorder with lower accuracy (67-77% versus 80%), and similar specificity (85-100% versus 93%), and sensitivity (0-58% versus 54%) in this sample. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.