{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:00:41Z","timestamp":1772906441358,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T00:00:00Z","timestamp":1604102400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T00:00:00Z","timestamp":1604102400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s11042-020-10119-w","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T16:02:59Z","timestamp":1604160179000},"page":"8127-8146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Speaker independent feature selection for speech emotion recognition: A multi-task approach"],"prefix":"10.1007","volume":"80","author":[{"given":"Elham","family":"Kalhor","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9802-2336","authenticated-orcid":false,"given":"Behzad","family":"Bakhtiari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,31]]},"reference":[{"key":"10119_CR1","doi-asserted-by":"crossref","unstructured":"Argyriou A, Evgeniou T, Pontil M (2007) Multi-task feature learning. In: Advances in neural information processing systems, pp 41\u201348","DOI":"10.7551\/mitpress\/7503.003.0010"},{"issue":"1","key":"10119_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183\u2013202","journal-title":"SIAM J Imaging Sci"},{"key":"10119_CR3","doi-asserted-by":"crossref","unstructured":"Burkhardt F, Paeschke A, Rolfes M, Sendlmeier WF, Weiss B (2005) A database of German emotional speech. In: Ninth European Conference on Speech Communication and Technology, pp 1516\u20131520","DOI":"10.21437\/Interspeech.2005-446"},{"key":"10119_CR4","doi-asserted-by":"crossref","unstructured":"Charoendee M, Suchato A, Punyabukkana P (2017) Speech emotion recognition using derived features from speech segment and kernel principal component analysis. In: Computer Science and Software Engineering (JCSSE), 2017 14th International Joint Conference on IEEE, pp 1\u20136","DOI":"10.1109\/JCSSE.2017.8025936"},{"issue":"99","key":"10119_CR5","first-page":"1","volume":"50","author":"L Chen","year":"2017","unstructured":"Chen L, Wu M, Zhou M, Liu Z, She J, Hirota K (2017) Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model. IEEE Trans Syst Man Cybern Syst 50(99):1\u201312","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"10119_CR6","doi-asserted-by":"crossref","unstructured":"Dang T, Sethu V, Ambikairajah E (2016) Factor analysis based speaker normalisation for continuous emotion prediction. In: INTERSPEECH, pp 913\u2013917","DOI":"10.21437\/Interspeech.2016-880"},{"issue":"8","key":"10119_CR7","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s00521-016-2712-y","volume":"29","author":"S Demircan","year":"2018","unstructured":"Demircan S, Kahramanli HJNC, Applications, (2018) Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech. Neural Comput Appl 29(8):59\u201366","journal-title":"Neural Comput Appl"},{"issue":"2","key":"10119_CR8","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1109\/JBHI.2017.2676878","volume":"22","author":"H Dibeklio\u011flu","year":"2018","unstructured":"Dibeklio\u011flu H, Hammal Z, Cohn JF (2018) Dynamic multimodal measurement of depression severity using deep autoencoding. IEEE J Biomed Health Inf 22(2):525\u2013536","journal-title":"IEEE J Biomed Health Inf"},{"issue":"1","key":"10119_CR9","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/TPAMI.2008.266","volume":"32","author":"S Escalera","year":"2010","unstructured":"Escalera S, Pujol O, Radeva P (2010) On the decoding process in ternary error-correcting output codes. IEEE Trans Pattern Anal Mach Intell 32(1):120\u2013134","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10119_CR10","doi-asserted-by":"crossref","unstructured":"Eyben F, W\u00f6llmer M, Schuller B (2010) Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM international conference on Multimedia. ACM, New York, pp 1459\u20131462","DOI":"10.1145\/1873951.1874246"},{"key":"10119_CR11","doi-asserted-by":"crossref","unstructured":"Farr\u00fas M, Ejarque P, Temko A, Hernando J (2007) Histogram equalization in svm multimodal person verification. In: International Conference on Biometrics. Springer, Berlin, pp 819\u2013827","DOI":"10.1007\/978-3-540-74549-5_86"},{"issue":"3","key":"10119_CR12","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1037\/0003-066X.56.3.218","volume":"56","author":"BL Fredrickson","year":"2001","unstructured":"Fredrickson BL (2001) The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am Psychol 56(3):218","journal-title":"Am Psychol"},{"issue":"5","key":"10119_CR13","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s00530-017-0547-8","volume":"25","author":"J Fu","year":"2019","unstructured":"Fu J, Mao Q, Tu J, Zhan Y (2019) Multimodal shared features learning for emotion recognition by enhanced sparse local discriminative canonical correlation analysis. Multimed Syst 25(5):451\u2013461","journal-title":"Multimed Syst"},{"issue":"Mar","key":"10119_CR14","first-page":"721","volume":"2","author":"J F\u00fcrnkranz","year":"2002","unstructured":"F\u00fcrnkranz J (2002) Round robin classification. J Mach Learn Res 2(Mar):721\u2013747","journal-title":"J Mach Learn Res"},{"key":"10119_CR15","doi-asserted-by":"crossref","unstructured":"Gajsek R, \u0160truc V, Miheli\u010d F (2010) Multi-modal emotion recognition using canonical correlations and acoustic features. In: 2010 20th International Conference on Pattern Recognition. IEEE, pp 4133\u20134136","DOI":"10.1109\/ICPR.2010.1005"},{"key":"10119_CR16","unstructured":"Gao L, Qi L, Chen E, Guan L (2014) A fisher discriminant framework based on Kernel Entropy Component Analysis for feature extraction and emotion recognition. In: 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) IEEE, pp 1\u20136"},{"key":"10119_CR17","unstructured":"Jin Y, Song P, Zheng W, Zhao L (2014) A feature selection and feature fusion combination method for speaker-independent speech emotion recognition. In: Acoustics, Speech and Signal Processing (ICASSP) (2014) IEEE International Conference on. IEEE, pp 4808\u20134812"},{"key":"10119_CR18","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1016\/j.neucom.2017.09.049","volume":"275","author":"H Kaya","year":"2018","unstructured":"Kaya H, Karpov AA (2018) Efficient and effective strategies for cross-corpus acoustic emotion recognition. Neurocomputing 275:1028\u20131034","journal-title":"Neurocomputing"},{"key":"10119_CR19","doi-asserted-by":"crossref","unstructured":"Kaya H, Eyben F, Salah AA, Schuller B (2014) CCA based feature selection with application to continuous depression recognition from acoustic speech features. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE, pp 3729\u20133733","DOI":"10.1109\/ICASSP.2014.6854298"},{"issue":"6","key":"10119_CR20","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1177\/0956797616647346","volume":"27","author":"BE Kok","year":"2016","unstructured":"Kok BE, Coffey KA, Cohn MA, Catalino LI, Vacharkulksemsuk T, Algoe SB, Brantley M, Fredrickson BL (2016) How positive emotions build physical health: Perceived positive social connections account for the upward spiral between positive emotions and vagal tone: Corrigendum. Psychol Sci 27(6):931","journal-title":"Psychol Sci"},{"issue":"2","key":"10119_CR21","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s10772-012-9127-7","volume":"15","author":"M Kotti","year":"2012","unstructured":"Kotti M, Patern\u00f2 F (2012) Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema. Int J Speech Technol 15(2):131\u2013150","journal-title":"Int J Speech Technol"},{"key":"10119_CR22","doi-asserted-by":"crossref","unstructured":"Kotti M, Paterno F, Kotropoulos C (2010) Speaker-independent negative emotion recognition. In: 2010 2nd International Workshop on Cognitive Information Processing IEEE, pp 417\u2013422","DOI":"10.1109\/CIP.2010.5604091"},{"key":"10119_CR23","unstructured":"Liu J, Ji S, Ye J (2012) Multi-task feature learning via efficient l2, 1-norm minimization. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp 339\u2013338"},{"key":"10119_CR24","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.neucom.2018.05.005","volume":"309","author":"Z-T Liu","year":"2018","unstructured":"Liu Z-T, Xie Q, Wu M, Cao W-H, Mei Y, Mao J-W (2018) Speech emotion recognition based on an improved brain emotion learning model. Neurocomputing 309:145\u2013156","journal-title":"Neurocomputing"},{"key":"10119_CR25","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neucom.2017.07.050","volume":"273","author":"Z-T Liu","year":"2018","unstructured":"Liu Z-T, Wu M, Cao W-H, Mao J-W, Xu J-P, Tan G-Z (2018) Speech emotion recognition based on feature selection and extreme learning machine decision tree. Neurocomputing 273:271\u2013280","journal-title":"Neurocomputing"},{"key":"10119_CR26","doi-asserted-by":"crossref","unstructured":"Lugger M, Yang B (2007) The relevance of voice quality features in speaker independent emotion recognition. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP\u201907, IEEE, pp 17\u201320","DOI":"10.1109\/ICASSP.2007.367152"},{"key":"10119_CR27","unstructured":"Martin O, Kotsia I, Macq B, Pitas I (2006) The enterface\u201905 audio-visual emotion database. In: Data Engineering Workshops (2006) Proceedings. 22nd International Conference on, IEEE, pp 8\u20138"},{"key":"10119_CR28","unstructured":"Nemirovskii A, Nesterov Y (1994) Interior point polynomial algorithms in convex programming. SIAM 36(4):682\u2013683"},{"key":"10119_CR29","doi-asserted-by":"crossref","unstructured":"Nicolaou MA, Panagakis Y, Zafeiriou S, Pantic M (2014) Robust canonical correlation analysis: Audio-visual fusion for learning continuous interest. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE, pp 1522\u20131526","DOI":"10.1109\/ICASSP.2014.6853852"},{"key":"10119_CR30","unstructured":"Obozinski G, Taskar B, Jordan M (2006) Multi-task feature selection. Statistics Department, Berkeley UC, Tech Rep 2 (2.2):2"},{"key":"10119_CR31","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","volume":"37","author":"S Poria","year":"2017","unstructured":"Poria S, Cambria E, Bajpai R, Hussain A (2017) A review of affective computing: From unimodal analysis to multimodal fusion. Inf Fusion 37:98\u2013125","journal-title":"Inf Fusion"},{"key":"10119_CR32","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1146\/annurev-clinpsy-032816-045252","volume":"13","author":"J Rottenberg","year":"2017","unstructured":"Rottenberg J (2017) Emotions in depression: What do we really know? Annu Rev Clin Psychol 13:241\u2013263","journal-title":"Annu Rev Clin Psychol"},{"issue":"2","key":"10119_CR33","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1007\/s10489-016-0823-x","volume":"46","author":"RR Sarvestani","year":"2017","unstructured":"Sarvestani RR, Boostani R (2017) FF-SKPCCA: Kernel probabilistic canonical correlation analysis. Appl Intell 46(2):438\u2013454","journal-title":"Appl Intell"},{"issue":"2","key":"10119_CR34","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/T-AFFC.2010.8","volume":"1","author":"B Schuller","year":"2010","unstructured":"Schuller B, Vlasenko B, Eyben F, Wollmer M, Stuhlsatz A, Wendemuth A, Rigoll G (2010) Cross-corpus acoustic emotion recognition: Variances and strategies. IEEE Trans Affect Comput 1(2):119\u2013131","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"10119_CR35","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TMM.2014.2375792","volume":"17","author":"C Shi","year":"2014","unstructured":"Shi C, Ruan Q, An G, Zhao R (2014) Hessian semi-supervised sparse feature selection based on L2, 1\/2 -matrix norm. IEEE Trans Multimed 17(1):16\u201328","journal-title":"IEEE Trans Multimed"},{"issue":"4","key":"10119_CR36","first-page":"39","volume":"8","author":"A Shirani","year":"2016","unstructured":"Shirani A, Nilchi ARN (2016) Speech emotion recognition based on SVM as both feature selector and classifier. Int J Image Graph Sig Process 8(4):39\u201345","journal-title":"Int J Image Graph Sig Process"},{"issue":"1","key":"10119_CR37","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00530-014-0390-0","volume":"22","author":"X Song","year":"2016","unstructured":"Song X, Zhang J, Han Y, Jiang J (2016) Semi-supervised feature selection via hierarchical regression for web image classification. Multimed Syst 22(1):41\u201349","journal-title":"Multimed Syst"},{"key":"10119_CR38","doi-asserted-by":"crossref","unstructured":"Tang J, Liu H (2012) Unsupervised feature selection for linked social media data. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 904\u2013912","DOI":"10.1145\/2339530.2339673"},{"issue":"01","key":"10119_CR39","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1142\/S1793351X13400023","volume":"7","author":"Z Xie","year":"2013","unstructured":"Xie Z, Guan L (2013) Multimodal information fusion of audio emotion recognition based on kernel entropy component analysis. Int J Semant Comput 7(01):25\u201342","journal-title":"Int J Semant Comput"},{"issue":"1","key":"10119_CR40","doi-asserted-by":"publisher","first-page":"37","DOI":"10.7305\/automatika.2016.07.853","volume":"57","author":"X Xu","year":"2016","unstructured":"Xu X, Huang C, Wu C, Zhao L (2016) Locally discriminant diffusion projection and its application in speech emotion recognition. Automatika 57(1):37\u201345","journal-title":"Automatika"},{"issue":"3","key":"10119_CR41","first-page":"1","volume":"10","author":"S Yaacob","year":"2015","unstructured":"Yaacob S, Muthusamy H, Polat K (2015) Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals. PLoS One 10(3):1\u201320","journal-title":"PLoS One"},{"issue":"5","key":"10119_CR42","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1016\/j.sigpro.2009.09.009","volume":"90","author":"B Yang","year":"2010","unstructured":"Yang B, Lugger M (2010) Emotion recognition from speech signals using new harmony features. Signal Process 90(5):1415\u20131423","journal-title":"Signal Process"},{"issue":"1","key":"10119_CR43","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s10772-016-9364-2","volume":"20","author":"N Yang","year":"2017","unstructured":"Yang N, Yuan J, Zhou Y, Demirkol I, Duan Z, Heinzelman W, Sturge-Apple M (2017) Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification. Int J Speech Technol 20(1):27\u201341","journal-title":"Int J Speech Technol"},{"issue":"3","key":"10119_CR44","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1093\/scan\/nsy012","volume":"13","author":"X Yang","year":"2018","unstructured":"Yang X, Garcia KM, Jung Y, Whitlow CT, McRae K, Waugh CE (2018) vmPFC activation during a stressor predicts positive emotions during stress recovery. Soc Cognit Affect Neurosci 13(3):256\u2013268","journal-title":"Soc Cognit Affect Neurosci"},{"key":"10119_CR45","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.compedu.2014.09.011","volume":"81","author":"Y-c Yeh","year":"2015","unstructured":"Yeh Y-c, Lai G-J, Lin CF, Lin C-W, Sun H-C (2015) How stress influences creativity in game-based situations: Analysis of stress hormones, negative emotions, and working memory. Comput Educ 81:143\u2013153","journal-title":"Comput Educ"},{"key":"10119_CR46","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.asoc.2017.03.013","volume":"56","author":"C Yogesh","year":"2017","unstructured":"Yogesh C, Hariharan M, Ngadiran R, Adom AH, Yaacob S, Polat K (2017) Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech. Appl Soft Comput 56:217\u2013232","journal-title":"Appl Soft Comput"},{"issue":"2","key":"10119_CR47","first-page":"676","volume":"62","author":"C Yogesh","year":"2017","unstructured":"Yogesh C, Hariharan M, Yuvaraj R, Ngadiran R, Yaacob S, Polat K (2017) Bispectral features and mean shift clustering for stress and emotion recognition from natural speech. Comput Electr Eng 62(2):676\u2013691","journal-title":"Comput Electr Eng"},{"issue":"1","key":"10119_CR48","first-page":"149","volume":"69","author":"C Yogesh","year":"2017","unstructured":"Yogesh C, Hariharan M, Ngadiran R, Adom AH, Yaacob S, Berkai C, Polat K (2017) A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal. Expert Syst Appl 69(1):149\u2013158","journal-title":"Expert Syst Appl"},{"issue":"2","key":"10119_CR49","doi-asserted-by":"publisher","first-page":"114","DOI":"10.5772\/55403","volume":"10","author":"S Zhang","year":"2013","unstructured":"Zhang S, Zhao X, Lei B (2013) Speech emotion recognition using an enhanced kernel isomap for human-robot interaction. Int J Adv Rob Syst 10(2):114","journal-title":"Int J Adv Rob Syst"},{"key":"10119_CR50","doi-asserted-by":"crossref","unstructured":"Zhang B, Provost EM, Essl G (2016) Cross-corpus acoustic emotion recognition from singing and speaking: A multi-task learning approach. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Piscataway, pp 5805\u20135809","DOI":"10.1109\/ICASSP.2016.7472790"},{"issue":"1","key":"10119_CR51","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TAFFC.2017.2684799","volume":"10","author":"B Zhang","year":"2017","unstructured":"Zhang B, Provost EM, Essl G (2017) Cross-corpus acoustic emotion recognition with multi-task learning: Seeking common ground while preserving differences. IEEE Trans Affect Comput 10(1):85\u201399","journal-title":"IEEE Trans Affect Comput"},{"key":"10119_CR52","unstructured":"Zhou J, Chen J, Ye J (2011) Malsar: Multi-task learning via structural regularization. Arizona State University, Tempe, 21"},{"key":"10119_CR53","doi-asserted-by":"crossref","unstructured":"Zou D, Wang J (2015) Speech recognition using locality preserving projection based on multi kernel learning supervision. In: 2015 International Symposium on Computers & Informatics, vol 2352-538X. Atlantis Press, Amsterdam, pp 1508\u20131516","DOI":"10.2991\/isci-15.2015.202"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10119-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-020-10119-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10119-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T02:52:21Z","timestamp":1696992741000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-020-10119-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,31]]},"references-count":53,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["10119"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10119-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,31]]},"assertion":[{"value":"4 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors have no potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}