{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:38:57Z","timestamp":1775263137608,"version":"3.50.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["645378 (ARIA-VALUSPA)"],"award-info":[{"award-number":["645378 (ARIA-VALUSPA)"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010686","name":"H2020 European Institute of Innovation and Technology","doi-asserted-by":"publisher","award":["797323 (HOL-DEEP-SENSE)"],"award-info":[{"award-number":["797323 (HOL-DEEP-SENSE)"]}],"id":[{"id":"10.13039\/100010686","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cybern."],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1109\/tcyb.2019.2901499","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T18:46:42Z","timestamp":1552589202000},"page":"1230-1239","source":"Crossref","is-referenced-by-count":20,"title":["A Generic Human\u2013Machine Annotation Framework Based on Dynamic Cooperative Learning"],"prefix":"10.1109","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8233-3014","authenticated-orcid":false,"given":"Yue","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Michi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisabeth","family":"Andre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6478-8699","authenticated-orcid":false,"given":"Bjorn","family":"Schuller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Weninger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"crossref","first-page":"823","DOI":"10.21437\/Interspeech.2012-250","article-title":"The automatic assessment of non-native prosody: Combining classical prosodic analysis with acoustic modelling","author":"h\u00f6nig","year":"2012","journal-title":"Proc INTERSPEECH"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2014.08.003"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/35016072","article-title":"Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit","volume":"405","author":"hahnloser","year":"2000","journal-title":"Nature"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557053"},{"key":"ref31","first-page":"1297","article-title":"Learning from crowds","volume":"11","author":"raykar","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref30","first-page":"889","article-title":"Supervised learning from multiple experts: Whom to trust when everyone lies a bit","author":"raykar","year":"2009","journal-title":"Proc 26th Int Conf Mach Learn"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1037\/0021-9010.69.1.85"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/584091.584093"},{"key":"ref35","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639344"},{"key":"ref28","first-page":"5","article-title":"The rise of crowdsourcing","volume":"14","author":"howe","year":"2006","journal-title":"Wired Mag"},{"key":"ref27","first-page":"137","article-title":"How many labellers? Modelling inter-labeller agreement and system performance for the automatic assessment of non-native prosody","author":"h\u00f6nig","year":"2010","journal-title":"Proc Workshop Speech and Language Technology in Education (SLATE '07)"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401965"},{"key":"ref2","article-title":"Active learning literature survey","author":"settles","year":"2010"},{"key":"ref1","article-title":"Semi-supervised learning literature survey","author":"zhu","year":"2006"},{"key":"ref20","first-page":"1861","article-title":"Probabilistic backpropagation for scalable learning of Bayesian neural networks","volume":"37","author":"hern\u00e1ndez-lobato","year":"2015","journal-title":"Proc ICML"},{"key":"ref22","first-page":"231","article-title":"Neural network ensembles, cross validation, and active learning","volume":"7","author":"krogh","year":"1995","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","first-page":"2348","article-title":"Practical variational inference for neural networks","volume":"24","author":"graves","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-77226-2_22"},{"key":"ref23","first-page":"483","article-title":"Information, prediction, and query by committee","author":"freund","year":"1993","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","first-page":"349","article-title":"Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis","author":"bliese","year":"2000","journal-title":"Multilevel Theory Research and Methods in Organizations"},{"key":"ref25","article-title":"Machine learning techniques for holistic computational paralinguistics","author":"zhang","year":"2018"},{"key":"ref50","author":"chollet","year":"2015","journal-title":"Keras"},{"key":"ref51","first-page":"3","article-title":"Theano: A CPU and GPU math compiler in Python","author":"bergstra","year":"2010","journal-title":"Proc Python in Science Conf"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-02714-2_14"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2824299"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2476658"},{"key":"ref52","author":"abadi","year":"2015","journal-title":"TensorFlow Large-Scale Machine Learning on Heterogeneous Systems"},{"key":"ref10","first-page":"5","article-title":"Agreement-based dynamic active learning with least and medium certainty query strategies","author":"zhang","year":"2015","journal-title":"Proc ICML Workshop Adv Active Learn Bridging Theory Pract"},{"key":"ref11","article-title":"Uncertainty in deep learning","author":"gal","year":"2016"},{"key":"ref40","first-page":"478","article-title":"The INTERSPEECH 2015 computational paralinguistics challenge: Degree of nativeness, parkinson’s & eating condition","author":"schuller","year":"2015","journal-title":"Proc INTERSPEECH"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1177\/1094428106296642"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2813"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/nature14541"},{"key":"ref15","first-page":"975","article-title":"Probability estimates for multi-class classification by pairwise coupling","volume":"5","author":"wu","year":"2004","journal-title":"J Mach Learn Res"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2007.367036"},{"key":"ref17","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc ICML"},{"key":"ref18","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)"},{"key":"ref19","first-page":"207","article-title":"Deep Gaussian processes","author":"damianou","year":"2013","journal-title":"Proc Workshop Artif Intell Statist"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.specom.2004.08.002"},{"key":"ref3","first-page":"58","article-title":"Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions","author":"zhu","year":"2003","journal-title":"Proc ICML Workshop Continuum Labeled to Unlabeled Data Machine Learning Data Mining"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"ref5","first-page":"115","article-title":"Cooperative learning and its application to emotion recognition from speech","volume":"23","author":"zhang","year":"2015","journal-title":"IEEE Trans Audio Speech Language Process"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2818346.2820774"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"ref49","author":"witten","year":"2005","journal-title":"Data Mining Practical Machine Learning Tools and Techniques"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACII.2015.7344553"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2013.00292"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502224"},{"key":"ref48","first-page":"359","article-title":"Correlation-based feature selection for discrete and numeric class machine learning","author":"hall","year":"2000","journal-title":"Proc 17th Int Conf Mach Learn (ICML)"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/2993148.2993155"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.specom.2009.01.004"},{"key":"ref41","first-page":"1328","article-title":"Assessing the prosody of non-native speakers of English: Measures and feature sets","author":"coutinho","year":"2016","journal-title":"Proc Language Resources Evaluation Conf (LREC)"},{"key":"ref44","first-page":"4","article-title":"Automatic assessment of non-native prosody for English as L2","author":"h\u00f6nig","year":"2010","journal-title":"Proc Speech Prosody"},{"key":"ref43","first-page":"21","article-title":"Automatic assessment of non-native prosody—Annotation, modelling and evaluation","author":"h\u00f6nig","year":"2012","journal-title":"Proc Int Symp Autom Detect Errors Pronunciation Training (IS ADEPT)"}],"container-title":["IEEE Transactions on Cybernetics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221036\/8964604\/08667368.pdf?arnumber=8667368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T20:46:01Z","timestamp":1663101961000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8667368\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3]]},"references-count":55,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tcyb.2019.2901499","relation":{},"ISSN":["2168-2267","2168-2275"],"issn-type":[{"value":"2168-2267","type":"print"},{"value":"2168-2275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3]]}}}