{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:29:45Z","timestamp":1742920185430,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031611391"},{"type":"electronic","value":"9783031611407"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-61140-7_24","type":"book-chapter","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T07:10:33Z","timestamp":1717053033000},"page":"245-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Stress Classification Model Using Speech: An Ambulatory Protocol-Based Database Study"],"prefix":"10.1007","author":[{"given":"Lara Eleonora","family":"Prado","sequence":"first","affiliation":[]},{"given":"Andrea","family":"Hongn","sequence":"additional","affiliation":[]},{"given":"Patricia","family":"Pelle","sequence":"additional","affiliation":[]},{"given":"Mar\u00eda Paula","family":"Bonomini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"24_CR1","unstructured":"Yaribeygi, H., Pahani, Y., Sahraei, H., Johnson, T., Sahebkar, A.: The impact of stress on body function: a review. EXCIL J. 16, 1057\u20131072 (2017)"},{"key":"24_CR2","doi-asserted-by":"publisher","unstructured":"Dimsdale, J.E.: Ashtary-Larky: psychological stress and cardiovascular disease. Int. J. Endocrinol. Metab. (2019). https:\/\/doi.org\/10.1016\/j.jacc.2007.12.024","DOI":"10.1016\/j.jacc.2007.12.024"},{"issue":"8","key":"24_CR3","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1111\/apt.17202","volume":"56","author":"J Black","year":"2022","unstructured":"Black, J., et al.: Systematic review: the role of psychological stress in inflammatory bowel disease. Aliment. Pharmacol. Ther. 56(8), 1235\u20131249 (2022). https:\/\/doi.org\/10.1111\/apt.17202","journal-title":"Aliment. Pharmacol. Ther."},{"key":"24_CR4","doi-asserted-by":"publisher","unstructured":"Afrisham, R., Paknejad, M., Soliemanifar, O., Sadegh-Nejadi, S., Meshkani, R., Ashtary-Larky, D.: The influence of psychological stress on the initiation and progression of diabetes and cancer. J. Am. Coll. Cardiol. 51(13) (2008). https:\/\/doi.org\/10.5812\/ijem.67400","DOI":"10.5812\/ijem.67400"},{"issue":"4","key":"24_CR5","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1111\/1467-8721.01248","volume":"12","author":"RL Claar","year":"2003","unstructured":"Claar, R.L., Blumenthal, J.A.: The value of stress-management interventions in life-threatening medical conditions. Curr. Dir. Psychol. Sci. 12(4), 133\u2013137 (2003). https:\/\/doi.org\/10.1111\/1467-8721.01248","journal-title":"Curr. Dir. Psychol. Sci."},{"issue":"9","key":"24_CR6","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1002\/cncr.31943","volume":"125","author":"MH Antoni","year":"2019","unstructured":"Antoni, M.H., Dhabhar, F.S.: The impact of psychosocial stress and stress management on immune responses in patients with cancer. Cancer 125(9), 1417\u20131431 (2019). https:\/\/doi.org\/10.1002\/cncr.31943","journal-title":"Cancer"},{"key":"24_CR7","doi-asserted-by":"publisher","unstructured":"Van Puyvelde, M., Neyt, X., McGlone, F., Pattyn, N.: Voice stress analysis: a new framework for voice and effort in human performance. Front. Psychol. 9, 1994 (2018). https:\/\/doi.org\/10.3389\/fpsyg.2018.01994","DOI":"10.3389\/fpsyg.2018.01994"},{"issue":"3","key":"24_CR8","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/s10772-018-09574-6","volume":"22","author":"L Tavi","year":"2018","unstructured":"Tavi, L.: Classifying females\u2019 stressed and neutral voices using acoustic-phonetic analysis of vowels: An exploratory investigation with emergency calls. Int. J. Speech Technol. 22(3), 511\u2013520 (2018). https:\/\/doi.org\/10.1007\/s10772-018-09574-6","journal-title":"Int. J. Speech Technol."},{"key":"24_CR9","doi-asserted-by":"publisher","unstructured":"Iyer, R., Nedeljkovic, M., Meyer, D.: Using vocal characteristics to classify psychological distress in adult helpline callers: retrospective observational study. JMIR Formative Res. 6(12), e42249 (2022). https:\/\/doi.org\/10.2196\/42249","DOI":"10.2196\/42249"},{"key":"24_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101945","volume":"100","author":"J G\u00f3rriz","year":"2023","unstructured":"G\u00f3rriz, J., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Info. Fusion 100, 101945 (2023)","journal-title":"Info. Fusion"},{"key":"24_CR11","doi-asserted-by":"publisher","unstructured":"Rodellar-Biarge, V., Palacios-Alonso, D., Nieto-Lluis, V., Gomez-Vilda, P.: Speech parameter selection for emotional stress characterization in women. In: 3rd IEEE International Work-Conference on Bioinspired Intelligence (2014). https:\/\/doi.org\/10.1109\/iwobi.2014.6913932","DOI":"10.1109\/iwobi.2014.6913932"},{"key":"24_CR12","doi-asserted-by":"publisher","unstructured":"Lu, H., et al.: StressSense: detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing (2012). https:\/\/doi.org\/10.1145\/2370216.2370270","DOI":"10.1145\/2370216.2370270"},{"key":"24_CR13","doi-asserted-by":"publisher","unstructured":"Iliev, A.I., Scordilis, M.S., Papa, J.P., Falc\u00e3o, A.X.: Spoken emotion recognition through optimum-path forest classification using glottal features. Comput. Speech Lang. 24(3), 445\u2013460 (2010). https:\/\/doi.org\/10.1016\/j.csl.2009.02.005","DOI":"10.1016\/j.csl.2009.02.005"},{"key":"24_CR14","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.yhbeh.2018.08.014","volume":"106","author":"K Pisanski","year":"2018","unstructured":"Pisanski, K., et al.: Multimodal Stress Detection: testing for covariation in vocal, hormonal and physiological responses to trier social stress test. Horm. Behav. 106, 52\u201361 (2018). https:\/\/doi.org\/10.1016\/j.yhbeh.2018.08.014","journal-title":"Horm. Behav."},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Ruiz, R., Absil, E., Harmegnies, B., Legros, C., Poch, D.: Time- and spectrum-related variabilities in stressed speech under laboratory and real conditions. Speech Commun. 20(1\u20132), 111\u2013129 (1996). https:\/\/doi.org\/10.1016\/s0167-6393(96)00048-9","DOI":"10.1016\/S0167-6393(96)00048-9"},{"key":"24_CR16","unstructured":"Vaikole, S., Mulajkar, S., More, A., Jayaswal, P., Dhas, S.: Stress detection through speech analysis using machine learning. In: IJCRT, vol. 8 (2020)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence for Neuroscience and Emotional Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61140-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T07:28:16Z","timestamp":1717054096000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61140-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031611391","9783031611407"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61140-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWINAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on the Interplay Between Natural and Artificial Computation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Olh\u00e2o","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwinac2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwinac.eu\/iwinac.org\/iwinac2024\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}