{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:11:20Z","timestamp":1787065880743,"version":"build-2736575974"},"reference-count":115,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["024.005.010"],"award-info":[{"award-number":["024.005.010"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Psychophysiology"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Speech production interferes with the measurement of changes in cardiac vagal activity during acute stress by attenuating the expected drop in heart rate variability. Speech also induces cardiac sympathetic changes similar to those induced by psychological stress. In the laboratory, confounding of physiological stress reactivity by speech may be controlled experimentally. In ambulatory assessments, however, detection of speech episodes would be necessary to separate the physiological effects of psychosocial stress from those of speech. Using machine learning (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/osf.io\/bk9nf\">https:\/\/osf.io\/bk9nf<\/jats:ext-link>\n                    ), we trained and tested speech classification models on data from 56 participants (ages 18\u201339) under controlled laboratory conditions. They were equipped with privacy\u2010secure wearables measuring thoracoabdominal respiratory inductance plethysmography (RIP from a single and a dual\u2010band set\u2010up), thoracic impedance pneumography, and an upper sternum positioned unit with triaxial accelerometers and gyroscopes. Following an 80\/20 train\u2010test split, nested cross\u2010validations were run with the machine learning algorithms XGBoost, gradient boosting, random forest, and logistic regression on the training set to get generalized performance estimates. Speech classification by the best model per method was then validated in the test set. Speech versus no\u2010speech classification performance (AUC) for both nested cross\u2010validation and test set predictions was excellent for thorax\u2013abdomen RIP (nested cross\u2010validation: 96.6%, test set prediction: 98.5%), thorax\u2010only RIP (97.5%, 99.1%), impedance (97.0%, 97.8%), and accelerometry (99.3%, 99.6%). The sternal accelerometer method outperformed others. These open\u2010access models leveraging biosignals have the potential to also work in daily life settings. This could enhance the trustworthiness of ambulatory psychophysiology, by enabling detection of speech and controlling for its confounding effects on physiology.\n                  <\/jats:p>","DOI":"10.1111\/psyp.70021","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T06:30:57Z","timestamp":1739514657000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Speech Detection via Respiratory Inductance Plethysmography, Thoracic Impedance, Accelerometers, and Gyroscopes: A Machine Learning\u2010Informed Comparative Study"],"prefix":"10.1111","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9944-6496","authenticated-orcid":false,"given":"Melisa","family":"Saygin","sequence":"first","affiliation":[{"name":"Department of Biological Psychology VU Amsterdam  Amsterdam the Netherlands"},{"name":"Amsterdam Public Health Research Institute Amsterdam UMC  Amsterdam the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myrte","family":"Schoenmakers","sequence":"additional","affiliation":[{"name":"Department of Biological Psychology VU Amsterdam  Amsterdam the Netherlands"},{"name":"Amsterdam Public Health Research Institute Amsterdam UMC  Amsterdam the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7867-1443","authenticated-orcid":false,"given":"Martin","family":"Gevonden","sequence":"additional","affiliation":[{"name":"Department of Biological Psychology VU Amsterdam  Amsterdam the Netherlands"},{"name":"Amsterdam Public Health Research Institute Amsterdam UMC  Amsterdam the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6022-2666","authenticated-orcid":false,"given":"Eco","family":"de Geus","sequence":"additional","affiliation":[{"name":"Department of Biological Psychology VU Amsterdam  Amsterdam the Netherlands"},{"name":"Amsterdam Public Health Research Institute Amsterdam UMC  Amsterdam the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.107281"},{"key":"e_1_2_9_3_1","first-page":"1","volume-title":"2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)","author":"Agarwal A. 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