{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T17:39:16Z","timestamp":1793813956733,"version":"4.6.1"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T00:00:00Z","timestamp":1720051200000},"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. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2024.1394210","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T00:54:44Z","timestamp":1720054484000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Depressive and mania mood state detection through voice as a biomarker using machine learning"],"prefix":"10.3389","volume":"15","author":[{"given":"Jun","family":"Ji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentian","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingzhu","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaonan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rujia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yantao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,7,4]]},"reference":[{"key":"ref1","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1016\/S0140-6736(13)60989-7","article-title":"Bipolar disorder diagnosis: challenges and future directions","volume":"381","author":"Phillips","year":"2013","journal-title":"Lancet"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/S2215-0366(18)30511-X","article-title":"Prevalence of mental disorders in China: a cross-sectional epidemiological study","volume":"6","author":"Huang","year":"2019","journal-title":"Lancet Psychiatry"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.bbi.2020.04.031","article-title":"The differential psychological distress of populations affected by the COVID-19 pandemic","volume":"87","author":"Zhang","year":"2020","journal-title":"Brain Behav Immun"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2147\/ndt.s19617","article-title":"The increasing burden of depression","volume":"7","author":"L\u00e9pine","year":"2011","journal-title":"Neuropsychiatr Dis Treat"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1049\/cit2.12113","article-title":"Automatic depression recognition by intelligent speech signal processing: a systematic survey","volume":"8","author":"Wu","year":"2023","journal-title":"CAAI Trans Intell Technol"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1002\/j.2167-4086.2015.00011.x","article-title":"Diagnosing the diagnostic and statistical manual of mental disorders. 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