Voice and speech as digital biomarkers of mental health: a scoping review of clinical interpretability, data collection and translational readiness

BackgroundVoice and speech encode acoustic, linguistic, and interactional information associated with mental health. Translating these signals into clinically meaningful digital biomarkers depends not only on predictive performance but on how speech is elicited, how the target state is defined, whether findings generalize beyond the original dataset, and whether features can be measured reliably in real-world settings.ObjectiveTo map voice, speech, language, and communication biomarkers studied across mental health outcomes and to examine three dimensions of translational readiness: clinical interpretability, evidentiary validity, and deployability.MethodsThis scoping review followed Joanna Briggs Institute guidance and PRISMA-ScR. PubMed, PsycINFO, and Scopus were searched through June 18, 2026. Two reviewers independently screened records and charted data. Biomarkers were classified as directly clinically interpretable, software-derived with a perceptual correlate, or technical-only.ResultsOf 1,524 records identified, 68 studies were included. Depression was examined in 56 studies (82%), followed by anxiety (n = 9), bipolar disorder or mania (n = 8), fatigue (n = 4), post-traumatic stress disorder (n = 3), stress (n = 2), and other outcomes including suicidal risk and loneliness (n = 10); no study examined burnout or emotional exhaustion. Controlled elicitation predominated (54/68, 79%) over naturalistic (16/68, 24%) and passive or ambient capture (3/68, 4%). One study focused on healthcare workers and one analyzed clinician–patient dyadic speech. Across depression studies, clinically interpretable patterns commonly included slower speech, more or longer pauses, lower or less variable pitch, reduced vocal intensity, flatter prosody, and reduced verbal output, although findings were not uniform. Validation on an independent or cross-dataset sample was reported by only 2 of 68 studies (3%).ConclusionsVoice-based mental health research identifies numerous candidate biomarkers, but translational readiness remains limited. Most evidence derives from depression and controlled speech tasks, and interpretability does not imply validity or deployability. Existing studies do not establish that candidate biomarkers remain valid in spontaneous, multispeaker, passive, or ambient clinical settings. Future work should prioritize validated outcome labels, longitudinal naturalistic capture, external validation, diverse populations, and explicit evaluation of interpretable and technical features across recording contexts.