{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T13:17:43Z","timestamp":1788355063491,"version":"build-2803163510"},"reference-count":42,"publisher":"ASME International","issue":"9","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This paper proposes a deep learning-based generalized empirical flow model (EFM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The approach is based on the assumption that the vibration of the vocal folds can be represented by a universal kinematics equation (UKE), which is used to generate a glottal shape library. For each shape in the library, the ground truth values of the flow rate and pressure distribution are obtained from the high-fidelity Navier\u2013Stokes (N\u2013S) solution. A fully connected deep neural network (DNN) is then trained to build the empirical mapping between the shapes and the flow rate and pressure distributions. The obtained DNN-based EFM is coupled with a finite element method (FEM)-based solid dynamics solver for fluid\u2013structure\u2013interaction (FSI) simulation of phonation. The EFM is evaluated by comparing the N-S solutions in both static glottal shapes and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency.<\/jats:p>","DOI":"10.1115\/1.4053862","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T09:17:25Z","timestamp":1645003045000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":2,"title":["A Deep Learning-Based Generalized Empirical Flow Model of Glottal Flow During Normal Phonation"],"prefix":"10.1115","volume":"144","author":[{"given":"Yang","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Maine , Orono, ME 04469"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weili","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Maine , 204 Crosby Hall, Orono, ME 04473"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luning","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Notre Dame , Notre Dame, IN 46556"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Notre Dame , Notre Dame, IN 46556"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Maine , Room 213 A, Boardman Hall, Orono, ME 04473"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Maine , Room 213, Boardman Hall, Orono, ME 04473"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"2026090208324102000_bib1","volume-title":"Principles of Voice Production","year":"2000"},{"issue":"1","key":"2026090208324102000_bib2","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1121\/1.2384846","article-title":"An In Vitro Setup to Test the Relevance and the Accuracy of Low-Order Vocal Folds Models","volume":"121","year":"2007","journal-title":"J. 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