{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:33:21Z","timestamp":1783557201606,"version":"3.55.0"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Covid-19 Special Research Grant","award":["CSRG008-2020ST"],"award-info":[{"award-number":["CSRG008-2020ST"]}]},{"name":"Impact Oriented Interdisciplinary Research Grant Programme","award":["IIRG002C-19HWB"],"award-info":[{"award-number":["IIRG002C-19HWB"]}]},{"name":"AUA-UAEU Joint Research Grant","award":["31R188"],"award-info":[{"award-number":["31R188"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00521-022-07410-2","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T11:12:51Z","timestamp":1653995571000},"page":"17581-17599","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Bidirectional parallel echo state network for speech emotion recognition"],"prefix":"10.1007","volume":"34","author":[{"given":"Hemin","family":"Ibrahim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7867-2665","authenticated-orcid":false,"given":"Chu Kiong","family":"Loo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fady","family":"Alnajjar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"issue":"13","key":"7410_CR1","doi-asserted-by":"publisher","first-page":"4653","DOI":"10.3390\/app10134653","volume":"10","author":"M Bojani\u0107","year":"2020","unstructured":"Bojani\u0107 M, Deli\u0107 V, Karpov A (2020) Call redistribution for a call center based on speech emotion recognition. Appl Sci 10(13):4653","journal-title":"Appl Sci"},{"key":"7410_CR2","doi-asserted-by":"crossref","unstructured":"Katsis CD, Rigas G, Goletsis Y, Fotiadis DI (2015) Emotion recognition in car industry. Emot Recognit A Pattern Anal Approach 515\u2013544","DOI":"10.1002\/9781118910566.ch20"},{"key":"7410_CR3","unstructured":"Al-Talabani A (2015) Automatic speech emotion recognition-feature space dimensionality and classification challenges. PhD thesis, University of Buckingham"},{"key":"7410_CR4","doi-asserted-by":"publisher","first-page":"115885","DOI":"10.1016\/j.eswa.2021.115885","volume":"187","author":"H P\u00e9rez-Espinosa","year":"2022","unstructured":"P\u00e9rez-Espinosa H, Guti\u00e9rrez-Seraf\u00edn B, Mart\u00ednez-Miranda J, Espinosa-Curiel IE (2022) Automatic children\u2019s personality assessment from emotional speech. Expert Syst Appl 187:115885. https:\/\/doi.org\/10.1016\/j.eswa.2021.115885","journal-title":"Expert Syst Appl"},{"issue":"8","key":"7410_CR5","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1109\/TMM.2014.2360798","volume":"16","author":"Q Mao","year":"2014","unstructured":"Mao Q, Dong M, Huang Z, Zhan Y (2014) Learning salient features for speech emotion recognition using convolutional neural networks. IEEE Trans Multimed 16(8):2203\u20132213","journal-title":"IEEE Trans Multimed"},{"key":"7410_CR6","doi-asserted-by":"crossref","unstructured":"Kathiresan T, Dellwo V (2019) Cepstral derivatives in mfccs for emotion recognition. In: 2019 IEEE 4th international conference on signal and image processing (ICSIP), pp 56\u201360. IEEE","DOI":"10.1109\/SIPROCESS.2019.8868573"},{"key":"7410_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/s21041249","author":"BJ Abbaschian","year":"2021","unstructured":"Abbaschian BJ, Sierra-Sosa D, Elmaghraby A (2021) Deep learning techniques for speech emotion recognition, from databases to models. Sensors. https:\/\/doi.org\/10.3390\/s21041249","journal-title":"Sensors"},{"key":"7410_CR8","doi-asserted-by":"publisher","first-page":"114177","DOI":"10.1016\/j.eswa.2020.114177","volume":"167","author":"S Mustaqeem Kwon","year":"2021","unstructured":"Mustaqeem Kwon S (2021) Mlt-dnet: speech emotion recognition using 1d dilated cnn based on multi-learning trick approach. Expert Syst Appl 167:114177. https:\/\/doi.org\/10.1016\/j.eswa.2020.114177","journal-title":"Expert Syst Appl"},{"key":"7410_CR9","doi-asserted-by":"publisher","first-page":"114683","DOI":"10.1016\/j.eswa.2021.114683","volume":"173","author":"D Li","year":"2021","unstructured":"Li D, Liu J, Yang Z, Sun L, Wang Z (2021) Speech emotion recognition using recurrent neural networks with directional self-attention. Expert Syst Appl 173:114683. https:\/\/doi.org\/10.1016\/j.eswa.2021.114683","journal-title":"Expert Syst Appl"},{"issue":"1","key":"7410_CR10","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TVT.2018.2879361","volume":"68","author":"Z Ma","year":"2019","unstructured":"Ma Z, Yu H, Chen W, Guo J (2019) Short utterance based speech language identification in intelligent vehicles with time-scale modifications and deep bottleneck features. IEEE Trans Veh Technol 68(1):121\u2013128. https:\/\/doi.org\/10.1109\/TVT.2018.2879361","journal-title":"IEEE Trans Veh Technol"},{"key":"7410_CR11","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.neunet.2017.02.013","volume":"92","author":"HM Fayek","year":"2017","unstructured":"Fayek HM, Lech M, Cavedon L (2017) Evaluating deep learning architectures for speech emotion recognition. Neural Netw 92:60\u201368","journal-title":"Neural Netw"},{"key":"7410_CR12","doi-asserted-by":"publisher","first-page":"107360","DOI":"10.1016\/j.apacoust.2020.107360","volume":"166","author":"F Daneshfar","year":"2020","unstructured":"Daneshfar F, Kabudian SJ, Neekabadi A (2020) Speech emotion recognition using hybrid spectral-prosodic features of speech signal\/glottal waveform, metaheuristic-based dimensionality reduction, and gaussian elliptical basis function network classifier. Appl Acoust 166:107360. https:\/\/doi.org\/10.1016\/j.apacoust.2020.107360","journal-title":"Appl Acoust"},{"key":"7410_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2016.08.081","volume":"373","author":"Q Ma","year":"2016","unstructured":"Ma Q, Shen L, Chen W, Wang J, Wei J, Yu Z (2016) Functional echo state network for time series classification. Inf Sci 373:1\u201320. https:\/\/doi.org\/10.1016\/j.ins.2016.08.081","journal-title":"Inf Sci"},{"key":"7410_CR14","unstructured":"Chen Y, Keogh E, Hu B, Begum N, Bagnall A, Mueen A, Batista G (2015) The ucr time series classification archive"},{"key":"7410_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/ACCESS.2021.3107858","volume":"1","author":"H Ibrahim","year":"2021","unstructured":"Ibrahim H, Loo CK, Alnajjar F (2021) Speech emotion recognition by late fusion for bidirectional reservoir computing with random projection. IEEE Access 1:18. https:\/\/doi.org\/10.1109\/ACCESS.2021.3107858","journal-title":"IEEE Access"},{"key":"7410_CR16","unstructured":"Wu Q, Fokoue E, Kudithipudi D (2018) On the statistical challenges of echo state networks and some potential remedies. arXiv:1802.07369"},{"key":"7410_CR17","doi-asserted-by":"crossref","unstructured":"Shoumy NJ, Ang L-M, Rahaman DM, Zia T, Seng KP, Khatun S (2021) Augmented audio data in improving speech emotion classification tasks. In: International conference on industrial, engineering and other applications of applied intelligent systems, pp 360\u2013365. Springer","DOI":"10.1007\/978-3-030-79463-7_30"},{"issue":"3","key":"7410_CR18","doi-asserted-by":"publisher","first-page":"526","DOI":"10.3390\/en11030526","volume":"11","author":"E L\u00f3pez","year":"2018","unstructured":"L\u00f3pez E, Valle C, Allende H, Gil E, Madsen H (2018) Wind power forecasting based on echo state networks and long short-term memory. Energies 11(3):526","journal-title":"Energies"},{"key":"7410_CR19","doi-asserted-by":"crossref","unstructured":"Scherer S, Oubbati M, Schwenker F, Palm G (2008) Real-time emotion recognition from speech using echo state networks. In: IAPR workshop on artificial neural networks in pattern recognition, pp 205\u2013216. Springer","DOI":"10.1007\/978-3-540-69939-2_20"},{"issue":"22","key":"7410_CR20","doi-asserted-by":"publisher","first-page":"6811","DOI":"10.1007\/s00500-016-2232-9","volume":"21","author":"A Rodan","year":"2017","unstructured":"Rodan A, Sheta AF, Faris H (2017) Bidirectional reservoir networks trained using svm + privileged information for manufacturing process modeling. Soft Comput 21(22):6811\u20136824","journal-title":"Soft Comput"},{"key":"7410_CR21","doi-asserted-by":"crossref","unstructured":"Bianchi FM, Scardapane S, L\u00f8kse S, Jenssen R (2020) Reservoir computing approaches for representation and classification of multivariate time series. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2020.3001377"},{"key":"7410_CR22","doi-asserted-by":"crossref","unstructured":"Gallicchio C, Micheli A (2019) Reservoir topology in deep echo state networks. In: International conference on artificial neural networks, pp 62\u201375. Springer","DOI":"10.1007\/978-3-030-30493-5_6"},{"key":"7410_CR23","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.specom.2019.10.004","volume":"115","author":"L Sun","year":"2019","unstructured":"Sun L, Zou B, Fu S, Chen J, Wang F (2019) Speech emotion recognition based on dnn-decision tree svm model. Speech Commun 115:29\u201337","journal-title":"Speech Commun"},{"issue":"4","key":"7410_CR24","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.jfds.2017.05.001","volume":"2","author":"G Zhong","year":"2016","unstructured":"Zhong G, Wang L-N, Ling X, Dong J (2016) An overview on data representation learning: from traditional feature learning to recent deep learning. J Financ Data Sci 2(4):265\u2013278","journal-title":"J Financ Data Sci"},{"key":"7410_CR25","doi-asserted-by":"publisher","first-page":"90368","DOI":"10.1109\/ACCESS.2019.2927384","volume":"7","author":"P Jiang","year":"2019","unstructured":"Jiang P, Fu H, Tao H, Lei P, Zhao L (2019) Parallelized convolutional recurrent neural network with spectral features for speech emotion recognition. IEEE Access 7:90368\u201390377. https:\/\/doi.org\/10.1109\/ACCESS.2019.2927384","journal-title":"IEEE Access"},{"key":"7410_CR26","doi-asserted-by":"publisher","unstructured":"Dai D, Wu Z, Li R, Wu X, Jia J, Meng H (2019) Learning discriminative features from spectrograms using center loss for speech emotion recognition. In: ICASSP 2019\u20142019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 7405\u20137409. https:\/\/doi.org\/10.1109\/ICASSP.2019.8683765","DOI":"10.1109\/ICASSP.2019.8683765"},{"key":"7410_CR27","doi-asserted-by":"publisher","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. MM \u201910, pp 1459\u20131462. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1873951.1874246","DOI":"10.1145\/1873951.1874246"},{"key":"7410_CR28","doi-asserted-by":"crossref","unstructured":"Amiriparian S, Gerczuk M, Ottl S, Cummins N, Freitag M, Pugachevskiy S, Baird A, Schuller B (2017) Snore sound classification using image-based deep spectrum features. In: Interspeech 2017, pp 3512\u20133516. ISCA","DOI":"10.21437\/Interspeech.2017-434"},{"key":"7410_CR29","doi-asserted-by":"publisher","unstructured":"Al-Talabani A, Sellahewa H, Jassim S (2013) Excitation source and low level descriptor features fusion for emotion recognition using svm and ann. In: 2013 5th computer science and electronic engineering conference (CEEC), pp 156\u2013161. https:\/\/doi.org\/10.1109\/CEEC.2013.6659464","DOI":"10.1109\/CEEC.2013.6659464"},{"issue":"8","key":"7410_CR30","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.3390\/s20082297","volume":"20","author":"Z-T Liu","year":"2020","unstructured":"Liu Z-T, Wu B-H, Li D-Y, Xiao P, Mao J-W (2020) Speech emotion recognition based on selective interpolation synthetic minority over-sampling technique in small sample environment. Sensors 20(8):2297","journal-title":"Sensors"},{"issue":"13","key":"7410_CR31","doi-asserted-by":"publisher","first-page":"5858","DOI":"10.1016\/j.eswa.2014.03.026","volume":"41","author":"CS Ooi","year":"2014","unstructured":"Ooi CS, Seng KP, Ang L-M, Chew LW (2014) A new approach of audio emotion recognition. Expert Syst Appl 41(13):5858\u20135869. https:\/\/doi.org\/10.1016\/j.eswa.2014.03.026","journal-title":"Expert Syst Appl"},{"key":"7410_CR32","doi-asserted-by":"publisher","unstructured":"Zhou S, Jia J, Wang Y, Chen W, Meng F, Li Y, Tao J (2018) Emotion inferring from large-scale internet voice data: A multimodal deep learning approach. In: 2018 first Asian conference on affective computing and intelligent interaction (ACII Asia), pp 1\u20136. https:\/\/doi.org\/10.1109\/ACIIAsia.2018.8470311","DOI":"10.1109\/ACIIAsia.2018.8470311"},{"key":"7410_CR33","doi-asserted-by":"publisher","unstructured":"Fu C, Dissanayake T, Hosoda K, Maekawa T, Ishiguro H (2020) Similarity of speech emotion in different languages revealed by a neural network with attention. In: 2020 IEEE 14th international conference on semantic computing (ICSC), pp 381\u2013386. https:\/\/doi.org\/10.1109\/ICSC.2020.00076","DOI":"10.1109\/ICSC.2020.00076"},{"issue":"6","key":"7410_CR34","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1016\/j.dsp.2012.05.007","volume":"22","author":"L Chen","year":"2012","unstructured":"Chen L, Mao X, Xue Y, Cheng LL (2012) Speech emotion recognition: features and classification models. Digit. Signal Process. 22(6):1154\u20131160. https:\/\/doi.org\/10.1016\/j.dsp.2012.05.007","journal-title":"Digit. Signal Process."},{"key":"7410_CR35","doi-asserted-by":"crossref","unstructured":"Lee J, Tashev I (2015) High-level feature representation using recurrent neural network for speech emotion recognition. In: INTERSPEECH, pp 1537\u20131540. ISCA, Dresden, Germany. http:\/\/dblp.uni-trier.de\/db\/conf\/interspeech\/interspeech2015.html","DOI":"10.21437\/Interspeech.2015-336"},{"issue":"1\/2","key":"7410_CR36","doi-asserted-by":"publisher","first-page":"14","DOI":"10.17743\/jaes.2019.0043","volume":"68","author":"N Vryzas","year":"2020","unstructured":"Vryzas N, Vrysis L, Matsiola M, Kotsakis R, Dimoulas C, Kalliris G (2020) Continuous speech emotion recognition with convolutional neural networks. J Audio Eng Soc 68(1\/2):14\u201324","journal-title":"J Audio Eng Soc"},{"key":"7410_CR37","unstructured":"Gallicchio C, Micheli A (2014) A preliminary application of echo state networks to emotion recognition. In: Fourth international workshop EVALITA 2014, pp 116\u2013119. Pisa University Press, Pisa, Italy"},{"key":"7410_CR38","doi-asserted-by":"publisher","unstructured":"Saleh Q, Merkel C, Kudithipudi D, Wysocki B (2015) Memristive computational architecture of an echo state network for real-time speech-emotion recognition. In: 2015 IEEE symposium on computational intelligence for security and defense applications (CISDA), pp 1\u20135. https:\/\/doi.org\/10.1109\/CISDA.2015.7208624","DOI":"10.1109\/CISDA.2015.7208624"},{"key":"7410_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2021.3062177","author":"Z Wang","year":"2021","unstructured":"Wang Z, Yao X, Huang Z, Liu L (2021) Deep echo state network with multiple adaptive reservoirs for time series prediction. IEEE Trans Cognit Dev Syst. https:\/\/doi.org\/10.1109\/TCDS.2021.3062177","journal-title":"IEEE Trans Cognit Dev Syst"},{"key":"7410_CR40","doi-asserted-by":"publisher","unstructured":"Gallicchio C, Micheli A, Pedrelli L (2017) Deep reservoir computing: A critical experimental analysis. Neurocomputing 268, 87\u201399. https:\/\/doi.org\/10.1016\/j.neucom.2016.12.089. Advances in artificial neural networks, machine learning and computational intelligence","DOI":"10.1016\/j.neucom.2016.12.089"},{"key":"7410_CR41","doi-asserted-by":"publisher","first-page":"107314","DOI":"10.1016\/j.asoc.2021.107314","volume":"106","author":"Z Huang","year":"2021","unstructured":"Huang Z, Yang C, Chen X, Zhou X, Chen G, Huang T, Gui W (2021) Functional deep echo state network improved by a bi-level optimization approach for multivariate time series classification. Appl Soft Comput 106:107314. https:\/\/doi.org\/10.1016\/j.asoc.2021.107314","journal-title":"Appl Soft Comput"},{"key":"7410_CR42","doi-asserted-by":"crossref","unstructured":"Wcis\u0142o R, Czech W (2021) Grouped multi-layer echo state networks with self-normalizing activations. In: International conference on computational science, pp 90\u201397. Springer","DOI":"10.1007\/978-3-030-77961-0_9"},{"issue":"3","key":"7410_CR43","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1109\/T-AFFC.2013.17","volume":"4","author":"Y Attabi","year":"2013","unstructured":"Attabi Y, Dumouchel P (2013) Anchor models for emotion recognition from speech. IEEE Trans Affect Comput 4(3):280\u2013290","journal-title":"IEEE Trans Affect Comput"},{"key":"7410_CR44","unstructured":"Bianchi FM, Scardapane S, L\u00f8kse S, Jenssen R (2017) Bidirectional deep-readout echo state networks. arXiv:1711.06509"},{"key":"7410_CR45","doi-asserted-by":"publisher","unstructured":"Li P, Hastie TJ, Church KW (2006) Very sparse random projections. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. KDD 06, pp 287\u2013296. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1150402.1150436","DOI":"10.1145\/1150402.1150436"},{"issue":"4","key":"7410_CR46","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","volume":"28","author":"S Davis","year":"1980","unstructured":"Davis S, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust Speech Signal Process 28(4):357\u2013366. https:\/\/doi.org\/10.1109\/TASSP.1980.1163420","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"issue":"8","key":"7410_CR47","first-page":"46","volume":"2","author":"M Babu","year":"2014","unstructured":"Babu M, Kumar MA, Santhosh S (2014) Extracting mfcc and gtcc features for emotion recognition from audio speech signals. Int J Res Comput Appl Robot 2(8):46\u201363","journal-title":"Int J Res Comput Appl Robot"},{"key":"7410_CR48","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Galar M, Prati RC, Krawczyk B, Herrera F (2018) Learning from imbalanced data sets vol. 10. Springer","DOI":"10.1007\/978-3-319-98074-4"},{"issue":"9","key":"7410_CR49","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284. https:\/\/doi.org\/10.1109\/TKDE.2008.239","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7410_CR50","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/s10618-012-0295-5","volume":"28","author":"G Menardi","year":"2012","unstructured":"Menardi G, Torelli N (2012) Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 28:92\u2013122","journal-title":"Data Min Knowl Discov"},{"issue":"5667","key":"7410_CR51","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.1091277","volume":"304","author":"H Jaeger","year":"2004","unstructured":"Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667):78\u201380. https:\/\/doi.org\/10.1126\/science.1091277","journal-title":"Science"},{"issue":"3","key":"7410_CR52","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127\u2013149. https:\/\/doi.org\/10.1016\/j.cosrev.2009.03.005","journal-title":"Comput Sci Rev"},{"key":"7410_CR53","doi-asserted-by":"publisher","unstructured":"Xue Y, Yang L, Haykin S (2007) Decoupled echo state networks with lateral inhibition. Neural Netw. 20(3), 365\u2013376. https:\/\/doi.org\/10.1016\/j.neunet.2007.04.014. Echo State Networks and Liquid State Machines","DOI":"10.1016\/j.neunet.2007.04.014"},{"issue":"4","key":"7410_CR54","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1109\/TCYB.2016.2533545","volume":"47","author":"ZK Malik","year":"2017","unstructured":"Malik ZK, Hussain A, Wu QJ (2017) Multilayered echo state machine: a novel architecture and algorithm. IEEE Trans Cybern 47(4):946\u2013959. https:\/\/doi.org\/10.1109\/TCYB.2016.2533545","journal-title":"IEEE Trans Cybern"},{"key":"7410_CR55","unstructured":"Chouikhi N, Ammar B, Alimi AM (2018) Genesis of basic and multi-layer echo state network recurrent autoencoders for efficient data representations. arXiv:1804.08996"},{"issue":"3","key":"7410_CR56","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s12559-017-9461-9","volume":"9","author":"C Gallicchio","year":"2017","unstructured":"Gallicchio C, Micheli A (2017) Echo state property of deep reservoir computing networks. Cognit Comput 9(3):337\u2013350","journal-title":"Cognit Comput"},{"key":"7410_CR57","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol. 25. Curran Associates, Inc"},{"key":"7410_CR58","doi-asserted-by":"publisher","unstructured":"Wu S, Falk TH, Chan W-Y (2011) Automatic speech emotion recognition using modulation spectral features. Speech Commun 53(5), 768\u2013785. https:\/\/doi.org\/10.1016\/j.specom.2010.08.013. Perceptual and Statistical Audition","DOI":"10.1016\/j.specom.2010.08.013"},{"key":"7410_CR59","doi-asserted-by":"crossref","unstructured":"Vlasenko B, Schuller B, Wendemuth A, Rigoll G (2007) Combining frame and turn-level information for robust recognition of emotions within speech. In: INTERSPEECH","DOI":"10.21437\/Interspeech.2007-611"},{"key":"7410_CR60","doi-asserted-by":"crossref","unstructured":"Burkhardt F, Paeschke A, Rolfes M, Sendlmeier W, Weiss B (2005) A database of German emotional speech. In: INTERSPEECH","DOI":"10.21437\/Interspeech.2005-446"},{"key":"7410_CR61","first-page":"398","volume-title":"Machine audition: principles, algorithms and systems","author":"S Haq","year":"2010","unstructured":"Haq S, Jackson PJB (2010) Multimodal emotion recognition. In: Wang W (ed) Machine audition: principles, algorithms and systems. IGI Global, Hershey PA, pp 398\u2013423"},{"key":"7410_CR62","doi-asserted-by":"crossref","unstructured":"Livingstone S, Russo F (2018) The ryerson audio-visual database of emotional speech and song (ravdess): a dynamic, multimodal set of facial and vocal expressions in north American english. PLoS ONE 13","DOI":"10.1371\/journal.pone.0196391"},{"key":"7410_CR63","unstructured":"Steidl S (2009) Automatic classification of emotion related user states in spontaneous children\u2019s speech. Logos-Verlag"},{"key":"7410_CR64","doi-asserted-by":"crossref","unstructured":"Schuller B, Steidl S, Batliner A (2009) The interspeech 2009 emotion challenge. In: Tenth annual conference of the international speech communication association","DOI":"10.21437\/Interspeech.2009-103"},{"key":"7410_CR65","doi-asserted-by":"publisher","unstructured":"Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. 45(4), 427\u2013437. https:\/\/doi.org\/10.1016\/j.ipm.2009.03.002","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"7410_CR66","doi-asserted-by":"crossref","unstructured":"Wen G, Li H, Huang J, Li D, Xun E (2017) Random deep belief networks for recognizing emotions from speech signals. Comput Intell Neurosci 2017","DOI":"10.1155\/2017\/1945630"},{"issue":"10","key":"7410_CR67","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1109\/LSP.2018.2860246","volume":"25","author":"M Chen","year":"2018","unstructured":"Chen M, He X, Yang J, Zhang H (2018) 3-d convolutional recurrent neural networks with attention model for speech emotion recognition. IEEE Signal Process Lett 25(10):1440\u20131444. https:\/\/doi.org\/10.1109\/LSP.2018.2860246","journal-title":"IEEE Signal Process Lett"},{"key":"7410_CR68","doi-asserted-by":"publisher","first-page":"125868","DOI":"10.1109\/ACCESS.2019.2938007","volume":"7","author":"H Meng","year":"2019","unstructured":"Meng H, Yan T, Yuan F, Wei H (2019) Speech emotion recognition from 3d log-mel spectrograms with deep learning network. IEEE Access 7:125868\u2013125881. https:\/\/doi.org\/10.1109\/ACCESS.2019.2938007","journal-title":"IEEE Access"},{"key":"7410_CR69","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.ins.2021.02.016","volume":"563","author":"Z-T Liu","year":"2021","unstructured":"Liu Z-T, Rehman A, Wu M, Cao W-H, Hao M (2021) Speech emotion recognition based on formant characteristics feature extraction and phoneme type convergence. Inf Sci 563:309\u2013325. https:\/\/doi.org\/10.1016\/j.ins.2021.02.016","journal-title":"Inf Sci"},{"key":"7410_CR70","doi-asserted-by":"publisher","first-page":"107721","DOI":"10.1016\/j.apacoust.2020.107721","volume":"173","author":"S Yildirim","year":"2021","unstructured":"Yildirim S, Kaya Y, K\u0131l\u0131\u00e7 F (2021) A modified feature selection method based on metaheuristic algorithms for speech emotion recognition. Appl Acoust 173:107721. https:\/\/doi.org\/10.1016\/j.apacoust.2020.107721","journal-title":"Appl Acoust"},{"key":"7410_CR71","doi-asserted-by":"publisher","unstructured":"Triantafyllopoulos A, Liu S, Schuller BW (2021) Deep speaker conditioning for speech emotion recognition. In: 2021 IEEE international conference on multimedia and expo (ICME), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME51207.2021.9428217","DOI":"10.1109\/ICME51207.2021.9428217"},{"key":"7410_CR72","doi-asserted-by":"publisher","first-page":"97515","DOI":"10.1109\/ACCESS.2019.2928625","volume":"7","author":"Z Zhao","year":"2019","unstructured":"Zhao Z, Bao Z, Zhao Y, Zhang Z, Cummins N, Ren Z, Schuller B (2019) Exploring deep spectrum representations via attention-based recurrent and convolutional neural networks for speech emotion recognition. IEEE Access 7:97515\u201397525. https:\/\/doi.org\/10.1109\/ACCESS.2019.2928625","journal-title":"IEEE Access"},{"key":"7410_CR73","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.neunet.2021.03.013","volume":"141","author":"Z Zhao","year":"2021","unstructured":"Zhao Z, Li Q, Zhang Z, Cummins N, Wang H, Tao J, W. Schuller B, (2021) Combining a parallel 2d cnn with a self-attention dilated residual network for ctc-based discrete speech emotion recognition. Neural Netw 141:52\u201360. https:\/\/doi.org\/10.1016\/j.neunet.2021.03.013","journal-title":"Neural Netw"},{"key":"7410_CR74","doi-asserted-by":"publisher","unstructured":"Shih P-Y, Chen C-P, Wang H-M (2017) Speech emotion recognition with skew-robust neural networks. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2751\u20132755. https:\/\/doi.org\/10.1109\/ICASSP.2017.7952657","DOI":"10.1109\/ICASSP.2017.7952657"},{"issue":"3","key":"7410_CR75","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1109\/TCYB.2017.2787717","volume":"49","author":"S Deb","year":"2019","unstructured":"Deb S, Dandapat S (2019) Multiscale amplitude feature and significance of enhanced vocal tract information for emotion classification. IEEE Trans Cybern 49(3):802\u2013815. https:\/\/doi.org\/10.1109\/TCYB.2017.2787717","journal-title":"IEEE Trans Cybern"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07410-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07410-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07410-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:15:31Z","timestamp":1744200931000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07410-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":75,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["7410"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07410-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]},"assertion":[{"value":"6 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}