{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T06:52:10Z","timestamp":1791183130754,"version":"4.1.0"},"reference-count":76,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Voice 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 1,524 records identified, 68 studies were included. Depression was examined in 56 studies (82%), followed by anxiety (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20099), bipolar disorder or mania (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20098), fatigue (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20094), post-traumatic stress disorder (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20093), stress (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092), and other outcomes including suicidal risk and loneliness (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910); 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\u2013patient 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%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Voice-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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1964389","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T06:19:51Z","timestamp":1791181191000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Voice and speech as digital biomarkers of mental health: a scoping review of clinical interpretability, data collection and translational readiness"],"prefix":"10.3389","volume":"8","author":[{"given":"Ralph Justin","family":"Uy","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University of British Columbia","place":["Vancouver, BC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Shaaban","sequence":"additional","affiliation":[{"name":"Faculty of Science, McGill University","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angel","family":"Arnaout","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of British Columbia","place":["Vancouver, BC, Canada"]},{"name":"Medical Informatics Division, Medical and Academic Affairs, Provincial Health Services Authority","place":["Vancouver, BC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1002\/lio2.354","article-title":"Automated assessment of psychiatric disorders using speech: a systematic review","volume":"5","author":"Low","year":"2020","journal-title":"Laryngoscope Investig Otolaryngol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.specom.2015.03.004","article-title":"A review of depression and suicide risk assessment using speech analysis","volume":"71","author":"Cummins","year":"2015","journal-title":"Speech Commun"},{"key":"B3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/IEMBS.2004.1403079","article-title":"Comparing objective feature statistics of speech for classifying clinical depression","volume":"2006","author":"Moore","year":"2004","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"B4","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1177\/2167702617747074","article-title":"In an absolute state: elevated use of absolutist words is a marker specific to anxiety, depression, and suicidal ideation","volume":"6","author":"Al-Mosaiwi","year":"2018","journal-title":"Clin Psychol Sci"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1038\/npp.2016.7","article-title":"Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health","volume":"41","author":"Onnela","year":"2016","journal-title":"Neuropsychopharmacology"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1750111","DOI":"10.3389\/fdgth.2026.1750111","article-title":"Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications","volume":"8","author":"Malo","year":"2026","journal-title":"Front Digit Health"},{"key":"B7","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.11124\/JBIES-20-00167","article-title":"Updated methodological guidance for the conduct of scoping reviews","volume":"18","author":"Peters","year":"2020","journal-title":"JBI Evid Synth"},{"key":"B8","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"B9","doi-asserted-by":"publisher","first-page":"e24191","DOI":"10.2196\/24191","article-title":"Measuring stress in health professionals over the phone using automatic speech analysis during the COVID-19 pandemic: observational pilot study","author":"K\u00f6nig","year":"2021","journal-title":"J Med Internet Res"},{"key":"B10","first-page":"1926","article-title":"Identifying Mood Episodes Using Dialogue Features from Clinical Interviews","volume-title":"Proceedings of the 20th Annual Conference of the International Speech Communication Association (Interspeech 2019)","author":"Aldeneh","year":"2019"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e856","DOI":"10.1038\/tp.2016.123","article-title":"Voice analysis as an objective state marker in bipolar disorder","volume":"6","author":"Faurholt-Jepsen","year":"2016","journal-title":"Transl Psychiatry"},{"key":"B12","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1111\/acps.13735","article-title":"Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: a prospective study","volume":"151","author":"Kaczmarek-Majer","year":"2025","journal-title":"Acta Psychiatr Scand"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1017\/S0033291719003994","article-title":"Deep learning-based automated speech detection as a marker of social functioning in late-life depression","volume":"51","author":"Little","year":"2021","journal-title":"Psychol Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"11604","DOI":"10.1038\/s41598-026-45965-5","article-title":"Automatic speech analysis can predict loneliness","volume":"16","author":"Immel","year":"2026","journal-title":"Sci Rep"},{"key":"B15","doi-asserted-by":"crossref","DOI":"10.1109\/IEMBS.2004.1403079","article-title":"Comparing objective feature statistics of speech for classifying clinical depression","volume-title":"The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","author":"Moore","year":"2004"},{"key":"B16","doi-asserted-by":"publisher","first-page":"994001","DOI":"10.3389\/fcell.2022.994001","article-title":"A rapid, non-invasive method for fatigue detection based on voice information","volume":"10","author":"Gao","year":"2022","journal-title":"Front Cell Dev Biol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.jad.2023.08.097","article-title":"Multilingual markers of depression in remotely collected speech samples: a preliminary analysis","volume":"341","author":"Cummins","year":"2023","journal-title":"J Affect Disord"},{"key":"B18","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.jad.2017.08.038","article-title":"Major depressive disorder discrimination using vocal acoustic features","volume":"225","author":"Taguchi","year":"2018","journal-title":"J Affect Disord"},{"key":"B19","first-page":"1676","article-title":"Effectiveness of voice quality features in detecting depression","volume-title":"Proceedings of the 19th Annual Conference of the International Speech Communication Association (Interspeech 2018)","author":"Afshan","year":"2018"},{"key":"B20","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.jad.2021.04.004","article-title":"Using i-vectors from voice features to identify major depressive disorder","volume":"288","author":"Di","year":"2021","journal-title":"J Affect Disord"},{"key":"B21","doi-asserted-by":"publisher","first-page":"4839334","DOI":"10.1155\/da\/4839334","article-title":"Method matters: enhancing voice-based depression detection with a new data collection framework","volume":"2025","author":"Vilenchik","year":"2025","journal-title":"Depress Anxiety"},{"key":"B22","doi-asserted-by":"publisher","first-page":"121563","DOI":"10.1016\/j.jad.2026.121563","article-title":"Beyond acoustic features: incorporating linguistic variables in automatic speech analysis for depression detection","volume":"405","author":"Maran","year":"2026","journal-title":"J Affect Disord"},{"key":"B23","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s41398-025-03728-2","article-title":"Objective speech measures capture depressive symptoms and associated cognitive difficulties","volume":"15","author":"Wiseman","year":"2025","journal-title":"Transl Psychiatry"},{"key":"B24","doi-asserted-by":"publisher","first-page":"121374","DOI":"10.1016\/j.jad.2026.121374","article-title":"Speech-derived acoustic biomarkers for depression: comprehensive cross-section and longitudinal analyses in different cohorts","volume":"403","author":"Lin","year":"2026","journal-title":"J Affect Disord"},{"key":"B25","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/j.biopsych.2012.03.015","article-title":"Vocal acoustic biomarkers of depression severity and treatment response","volume":"72","author":"Mundt","year":"2012","journal-title":"Biol Psychiatry"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e64578","DOI":"10.2196\/64578","article-title":"Momentary depression severity prediction in patients with acute depression who undergo sleep deprivation therapy: speech-based machine learning approach","volume":"11","author":"Hartnagel","year":"2024","journal-title":"JMIR Ment Health"},{"key":"B27","doi-asserted-by":"publisher","first-page":"e49222","DOI":"10.2196\/49222","article-title":"Speech features as predictors of momentary depression severity in patients with depressive disorder undergoing sleep deprivation therapy: ambulatory assessment pilot study","volume":"11","author":"Wadle","year":"2024","journal-title":"JMIR Ment Health"},{"key":"B28","doi-asserted-by":"publisher","first-page":"e322","DOI":"10.1017\/S0033291725102274","article-title":"Cross-sectional and longitudinal associations between anxiety and acoustic-prosodic markers in adolescents","volume":"55","author":"Ciampelli","year":"2025","journal-title":"Psychol Med"},{"key":"B29","doi-asserted-by":"publisher","first-page":"e36828","DOI":"10.2196\/36828","article-title":"Acoustic and linguistic features of impromptu speech and their association with anxiety: validation study","volume":"9","author":"Teferra","year":"2022","journal-title":"JMIR Ment Health"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1135469","DOI":"10.3389\/fpsyg.2023.1135469","article-title":"A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations","volume":"14","author":"Cohen","year":"2023","journal-title":"Front Psychol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1079448","DOI":"10.3389\/fpsyt.2023.1079448","article-title":"Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls","volume":"14","author":"Pan","year":"2023","journal-title":"Front Psychiatry"},{"key":"B32","doi-asserted-by":"publisher","first-page":"610","DOI":"10.3758\/BF03193033","article-title":"Fatigue estimation using voice analysis","volume":"39","author":"Greeley","year":"2007","journal-title":"Behav Res Methods"},{"key":"B33","doi-asserted-by":"publisher","first-page":"S120","DOI":"10.1016\/j.mjafi.2022.12.001","article-title":"Harmonic-to-noise ratio as speech biomarker for fatigue: k-nearest neighbour machine learning algorithm","volume":"80","author":"Gaur","year":"2024","journal-title":"Med J Armed Forces India"},{"key":"B34","doi-asserted-by":"publisher","first-page":"e58572","DOI":"10.2196\/58572","article-title":"Automated speech analysis for risk detection of depression, anxiety, insomnia, and fatigue: algorithm development and validation study","volume":"26","author":"Riad","year":"2024","journal-title":"J Med Internet Res"},{"key":"B35","first-page":"1","volume-title":"Changes in Frequency spectrum of Vowels due to Psychological stressIn: 20th International Conference Radioelektronika","author":"Sigmund","year":"2010"},{"key":"B36","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1002\/da.22890","article-title":"Speech-based markers for posttraumatic stress disorder in US veterans","volume":"36","author":"Marmar","year":"2019","journal-title":"Depress Anxiety"},{"key":"B37","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1109\/OJEMB.2023.3284798","article-title":"An emotion-driven vocal biomarker-based PTSD screening tool","volume":"5","author":"Quatieri","year":"2024","journal-title":"IEEE Open J Eng Med Biol"},{"key":"B38","doi-asserted-by":"publisher","first-page":"1509206","DOI":"10.3389\/fpsyg.2025.1509206","article-title":"Sex differences in PTSD speech biomarkers assessed by virtual agent-induced conversations","volume":"16","author":"Menne","year":"2025","journal-title":"Front Psychol"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2024.08.006","article-title":"Comparison of acoustic parameters of voice and speech according to vowel type and suicidal risk in adolescents","author":"Figueroa","year":"","journal-title":"J Voice"},{"key":"B40","first-page":"1104","article-title":"Timing patterns of speech as potential indicators of near-term suicidal risk","volume":"3","author":"Nik Nur Wahidah","year":"2015","journal-title":"Int J Multidiscipl Curr Res"},{"key":"B41","doi-asserted-by":"publisher","first-page":"100659","DOI":"10.1016\/j.smhl.2026.100659","article-title":"Acoustic signatures of anxiety and depression in academic stress contexts: evidence from voice analysis in pre-university students","volume":"40","author":"Abutara","year":"2026","journal-title":"Smart Health"},{"key":"B42","first-page":"p. 141","article-title":"From joyous to clinically depressed: mood detection using spontaneous speech","volume-title":"Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference (FLAIRS-25)","author":"Alghowinem","year":"2012"},{"key":"B43","first-page":"p. 8022","volume-title":"A Comparative study of Different classifiers for Detecting depression from Spontaneous speech2013 IEEE International Conference on Acoustics, Speech and Signal Processing","author":"Alghowinem","year":"2013"},{"key":"B44","first-page":"2534","article-title":"Characterising depressed speech for classification","volume-title":"Proceedings of the 14th Annual Conference of the International Speech Communication Association (Interspeech 2013)","author":"Alghowinem","year":"2013"},{"key":"B45","doi-asserted-by":"publisher","first-page":"489","DOI":"10.24869\/psyd.2023.489","article-title":"A new artificial intelligence-based clinical decision support system for diagnosis of major psychiatric diseases based on voice analysis","volume":"35","author":"Cansel","year":"2023","journal-title":"Psychiatr Danub"},{"key":"B46","doi-asserted-by":"publisher","first-page":"e65555","DOI":"10.2196\/65555","article-title":"Acoustic and natural language markers for bipolar disorder: a pilot, mHealth cross-sectional study","volume":"9","author":"Crocamo","year":"2025","journal-title":"JMIR Form Res"},{"key":"B47","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.specom.2015.09.003","article-title":"Analysis of acoustic space variability in speech affected by depression","volume":"75","author":"Cummins","year":"2015","journal-title":"Speech Commun"},{"key":"B48","doi-asserted-by":"publisher","first-page":"2686","DOI":"10.1038\/s41380-024-02877-y","article-title":"Unraveling the associations between voice pitch and major depressive disorder: a multisite genetic study","volume":"30","author":"Di","year":"2025","journal-title":"Mol Psychiatry"},{"key":"B49","doi-asserted-by":"publisher","first-page":"121958","DOI":"10.1016\/j.jad.2026.121958","article-title":"Acoustics of depression","volume":"410","author":"Dumpala","year":"2026","journal-title":"J Affect Disord"},{"key":"B50","doi-asserted-by":"publisher","first-page":"20552076231180523","DOI":"10.1177\/20552076231180523","article-title":"Evaluating the utility of daily speech assessments for monitoring depression symptoms","volume":"9","author":"Gumus","year":"2023","journal-title":"Digit Health"},{"key":"B51","doi-asserted-by":"publisher","first-page":"256.e1","DOI":"10.1016\/j.jvoice.2016.06.006","article-title":"Evaluation of voice acoustics as predictors of clinical depression scores","volume":"31","author":"Hashim","year":"2017","journal-title":"J Voice"},{"key":"B52","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.1109\/TAFFC.2025.3564531","article-title":"Detection of schizophrenia spectrum disorder and major depression disorder using automated speech analysis","volume":"16","author":"Hi\u00df","year":"2025","journal-title":"IEEE Trans Affective Comput"},{"key":"B53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/6508319","article-title":"Detecting depression using an ensemble logistic regression model based on multiple speech features","volume":"2018","author":"Jiang","year":"2018","journal-title":"Comput Math Methods Med"},{"key":"B54","doi-asserted-by":"publisher","first-page":"e34474","DOI":"10.2196\/34474","article-title":"Automatic depression detection using smartphone-based text-dependent speech signals: deep convolutional neural network approach","volume":"25","author":"Kim","year":"2023","journal-title":"J Med Internet Res"},{"key":"B55","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1186\/s12888-022-04475-0","article-title":"Detecting subtle signs of depression with automated speech analysis in a non-clinical sample","volume":"22","author":"K\u00f6nig","year":"2022","journal-title":"BMC Psychiatry"},{"key":"B56","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.jad.2021.04.098","article-title":"Screening major depressive disorder using vocal acoustic features in the elderly by sex","volume":"291","author":"Lee","year":"2021","journal-title":"J Affect Disord"},{"key":"B57","doi-asserted-by":"publisher","first-page":"1016676","DOI":"10.3389\/fpsyt.2022.1016676","article-title":"A deep learning-based model for detecting depression in senior population","volume":"13","author":"Lin","year":"2022","journal-title":"Front Psychiatry"},{"key":"B58","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1370\/afm.240091","article-title":"Evaluation of an AI-based voice biomarker tool to detect signals consistent with moderate to severe depression","volume":"23","author":"Mazur","year":"2025","journal-title":"Ann Fam Med"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-024-06253-6","article-title":"The voice of depression: speech features as biomarkers for major depressive disorder","author":"Menne","year":"2024","journal-title":"BMC Psychiatry"},{"key":"B60","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s44192-025-00138-0","article-title":"Voice analysis and deep learning for detecting mental disorders in pregnant women: a cross-sectional study","volume":"5","author":"Ooba","year":"2025","journal-title":"Discov Ment Health"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2025.09.012","article-title":"Voice of mind, a deep learning model for depression and anxiety assessment from acoustic and lexical vocal biomarkers","author":"Regondi","year":"","journal-title":"J Voice"},{"key":"B62","first-page":"1","volume-title":"Speech Analysis and DepressionIn: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","author":"Tan","year":"2016"},{"key":"B63","doi-asserted-by":"publisher","first-page":"3046","DOI":"10.3390\/jcm10143046","article-title":"Detection of minor and major depression through voice as a biomarker using machine learning","volume":"10","author":"Shin","year":"2021","journal-title":"JCM"},{"key":"B64","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.jvoice.2021.06.018","article-title":"Voice acoustic parameters as predictors of depression","volume":"38","author":"Silva","year":"2024","journal-title":"J Voice"},{"key":"B65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.specom.2019.10.003","article-title":"Automatic depression classification based on affective read sentences: opportunities for text-dependent analysis","volume":"115","author":"Stasak","year":"2019","journal-title":"Speech Commun"},{"key":"B66","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/MPRV.2022.3163656","article-title":"Breaking age barriers with automatic voice-based depression detection","volume":"21","author":"Stasak","year":"2022","journal-title":"IEEE Pervasive Comput"},{"key":"B67","doi-asserted-by":"publisher","first-page":"e37061","DOI":"10.2196\/37061","article-title":"Digital content-free speech analysis tool to measure affective distress in mental health: evaluation study","volume":"6","author":"Tonn","year":"2022","journal-title":"JMIR Form Res"},{"key":"B68","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/ICTAI52525.2021.00054","article-title":"A lightweight machine learning approach to detect depression from speech analysis","volume-title":"2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)","author":"Verde","year":"2021"},{"key":"B69","doi-asserted-by":"publisher","first-page":"5388","DOI":"10.1007\/s12144-025-07313-0","article-title":"Multimodal analysis of vocal emotion and prosody in subclinical depressed individuals","volume":"44","author":"Wang","year":"2025","journal-title":"Curr Psychol"},{"key":"B70","doi-asserted-by":"publisher","first-page":"1195276","DOI":"10.3389\/fpsyt.2023.1195276","article-title":"Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study","volume":"14","author":"Wang","year":"2023","journal-title":"Front Psychiatry"},{"key":"B71","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1016\/j.jad.2022.12.117","article-title":"Development and validation of a machine learning-based vocal predictive model for major depressive disorder","volume":"325","author":"Wasserzug","year":"2023","journal-title":"J Affect Disord"},{"key":"B72","doi-asserted-by":"publisher","first-page":"1561332","DOI":"10.3389\/fpubh.2025.1561332","article-title":"Acoustic-based machine learning approaches for depression detection in Chinese university students","volume":"13","author":"Wei","year":"2025","journal-title":"Front Public Health"},{"key":"B73","doi-asserted-by":"publisher","first-page":"e0238726","DOI":"10.1371\/journal.pone.0238726","article-title":"Using speech recognition technology to investigate the association between timing-related speech features and depression severity. Hashimoto K, ed","volume":"15","author":"Yamamoto","year":"2020","journal-title":"PLoS One"},{"key":"B74","doi-asserted-by":"publisher","first-page":"101883","DOI":"10.1016\/j.jbtep.2023.101883","article-title":"Emotion-dependent language featuring depression","volume":"81","author":"Yang","year":"2023","journal-title":"J Behav Ther Exp Psychiatry"},{"key":"B75","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1002\/da.23020","article-title":"Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative","volume":"37","author":"Zhang","year":"2020","journal-title":"Depress Anxiety"},{"key":"B76","doi-asserted-by":"publisher","first-page":"815678","DOI":"10.3389\/fpsyt.2022.815678","article-title":"Vocal acoustic features as potential biomarkers for identifying\/diagnosing depression: a cross-sectional study","volume":"13","author":"Zhao","year":"2022","journal-title":"Front Psychiatry"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1964389\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T06:19:54Z","timestamp":1791181194000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1964389\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,5]]},"references-count":76,"alternative-id":["10.3389\/fdgth.2026.1964389"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2026.1964389","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,5]]},"article-number":"1964389"}}