ArticleJMIR mental health2022
Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study.
Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Speech-Derived Digital Markers of Treatment Response and Improvement Trajectories in Pediatric Anxiety.Research square · 2026Article
- Psychosocial Stress in the Chinese Community: Speech Analytics Through Linguistic and Acoustic Fusion Using Machine Learning.JMIR biomedical engineering · 2026Article
- Detecting risk for depression and anxiety symptoms among older bereaved women using text-based analysis in the Nurses' Health Study II.American journal of epidemiology · 2026Article
- Article
- Cross-sectional and longitudinal associations between anxiety and acoustic-prosodic markers in adolescents.Psychological medicine · 2025Article
- Leveraging large language models for automated depression screening.PLOS digital health · 2025Article
- Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Investigating Health and Well-Being Challenges Faced by an Aging Workforce in the Construction and Nursing Industries: Computational Linguistic Analysis of Twitter Data.Journal of medical Internet research · 2024Article
- Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study.JMIR mental health · 2023Article
- Detection of Suicide Risk Using Vocal Characteristics: Systematic Review.JMIR biomedical engineering · 2022Review
- Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders.JMIR mental health · 2022Article
- Screening for Generalized Anxiety Disorder From Acoustic and Linguistic Features of Impromptu Speech: Prediction Model Evaluation Study.JMIR formative research · 2022Article
- Speech markers of depression dimensions across cognitive status.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Speech and language features as indicators of neuropsychiatric symptoms in a memory clinic population.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring.
objectiveThis study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity.
methodsA large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates.
resultsWord count and speaking duration were negatively correlated with anxiety scores (r=-0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=-0.09; P=.01).
conclusionsBoth acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment.
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