{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:39:55Z","timestamp":1778258395111,"version":"3.51.4"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"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":["Int J Speech Technol"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s10772-020-09776-x","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T08:03:18Z","timestamp":1606204998000},"page":"155-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Optimal feature selection for speech emotion recognition using enhanced cat swarm optimization algorithm"],"prefix":"10.1007","volume":"24","author":[{"given":"M.","family":"Gomathy","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"issue":"3","key":"9776_CR4","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.csl.2010.10.001","volume":"25","author":"EM Albornoz","year":"2011","unstructured":"Albornoz, E. M., Milone, D. H., & Rufiner, H. L. (2011). Spoken emotion recognition using hierarchical classifiers. Computer Speech & Language, 25(3), 556\u2013570.","journal-title":"Computer Speech & Language"},{"key":"9776_CR5","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.patcog.2010.09.020","volume":"44","author":"M El Ayadi","year":"2011","unstructured":"El Ayadi, M., Kamel, M. S., & Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition, 44, 572\u2013587.","journal-title":"Pattern Recognition"},{"issue":"8","key":"9776_CR6","doi-asserted-by":"publisher","first-page":"2115","DOI":"10.1007\/s00521-011-0643-1","volume":"21","author":"D Gharavian","year":"2012","unstructured":"Gharavian, D., Mansour, S., Alireza, N., & Sahar, G. (2012). Speech emotion recognition using FCBF feature selection method and GA-optimized fuzzy ARTMAP neural network. Neural Computing and Applications, 21(8), 2115\u20132126.","journal-title":"Neural Computing and Applications"},{"key":"9776_CR7","doi-asserted-by":"publisher","first-page":"90368","DOI":"10.1109\/ACCESS.2019.2927384","volume":"7","author":"P Jiang","year":"2019","unstructured":"Jiang, P., Hongliang, F., Huawei, T., Peizhi, L., & Li, Z. (2019). Parallelized Convolutional Recurrent Neural Network With Spectral Features for Speech Emotion Recognition. IEEE Access, 7, 90368\u201390377.","journal-title":"IEEE Access"},{"key":"9776_CR8","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.dsp.2017.10.016","volume":"72","author":"S Jing","year":"2018","unstructured":"Jing, S., Xia, M., & Lijiang, C. (2018). Prominence features: Effective emotional features for speech emotion recognition. Digital Signal Processing, 72, 216\u2013231.","journal-title":"Digital Signal Processing"},{"key":"9776_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.specom.2019.04.004","volume":"110","author":"X Li","year":"2019","unstructured":"Li, X., & Masato, A. (2019). Improving multilingual speech emotion recognition by combining acoustic features in a three-layer model. Speech Communication, 110, 1\u201312.","journal-title":"Speech Communication"},{"key":"9776_CR10","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., Min, W., Wei-Hua, C., Jun-Wei, M., Jian-Ping, X., & Guan-Zheng, T. (2018). Speech emotion recognition based on feature selection and extreme learning machine decision tree. Neurocomputing, 273, 271\u2013280.","journal-title":"Neurocomputing"},{"key":"9776_CR11","doi-asserted-by":"publisher","first-page":"125868","DOI":"10.1109\/ACCESS.2019.2938007","volume":"7","author":"H Meng","year":"2019","unstructured":"Meng, H., Tianhao, Y., Fei, Y., & Hongwei, W. (2019). Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network. IEEE Access, 7, 125868\u2013125881.","journal-title":"IEEE Access"},{"issue":"4","key":"9776_CR12","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s10772-015-9294-4","volume":"18","author":"A Milton","year":"2015","unstructured":"Milton, A., & Tamil, S. S. (2015). Four-stage feature selection to recognize emotion from speech signals. International Journal of Speech Technology, 18(4), 505\u2013520.","journal-title":"International Journal of Speech Technology"},{"key":"9776_CR13","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.apacoust.2018.11.028","volume":"146","author":"T Ozseven","year":"2019","unstructured":"Ozseven, T. (2019). A novel feature selection method for speech emotion recognition. Applied Acoustics, 146, 320\u2013326.","journal-title":"Applied Acoustics"},{"key":"9776_CR1","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, S., Emary, I. M. M. E. I. (2013). Speech emotion recognition approaches in human computer interaction.Telecommunication Systems, 52(3), 1467\u20131478.","DOI":"10.1007\/s11235-011-9624-z"},{"issue":"1","key":"9776_CR14","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s00521-012-0814-8","volume":"23","author":"M Sheikhan","year":"2013","unstructured":"Sheikhan, M., Mahdi, B., & Davood, G. (2013). Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method. Neural Computing and Applications, 23(1), 215\u2013227.","journal-title":"Neural Computing and Applications"},{"key":"9776_CR2","doi-asserted-by":"crossref","unstructured":"Sun, L., Sheng, F., and Fu, W. (2019) Decision tree SVM model with Fisher feature selection for speech emotion recognition. EURASIP Journal on Audio, Speech, and Music Processing, 1(2).","DOI":"10.1186\/s13636-018-0145-5"},{"issue":"3","key":"9776_CR15","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s10772-015-9272-x","volume":"18","author":"Y Sun","year":"2015","unstructured":"Sun, Y., & Guihua, W. (2015). Emotion recognition using semi-supervised feature selection with speaker normalization. International Journal of Speech Technology, 18(3), 317\u2013331.","journal-title":"International Journal of Speech Technology"},{"issue":"75","key":"9776_CR16","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-642-24571-8_12","volume":"69","author":"F Wang","year":"2011","unstructured":"Wang, F., Verhelst, W., & Sahli, H. (2011). Relevance vector machine based speech emotion recognition\u201d Lecture Notes in Computer Science. Affect Comput Intell Interact, 69(75), 111\u2013120.","journal-title":"Affect Comput Intell Interact"},{"key":"9776_CR3","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Dellandrea, E., Dou, W., Chen, L. (2010). Multi-stage classification of emotional speech motivated by a dimensional model.Multimedia Tools and Applications, 46, 119\u2013345.","DOI":"10.1007\/s11042-009-0319-3"},{"key":"9776_CR17","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.bspc.2018.08.035","volume":"47","author":"J Zhao","year":"2019","unstructured":"Zhao, J., Xia, M., & Lijiang, C. (2019a). Speech emotion recognition using deep 1D & 2D CNN LSTM networks. Biomedical Signal Processing and Control, 47, 312\u2013323.","journal-title":"Biomedical Signal Processing and Control"},{"key":"9776_CR18","doi-asserted-by":"publisher","first-page":"97515","DOI":"10.1109\/ACCESS.2019.2928625","volume":"7","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z., Zhongtian, B., Yiqin, Z., Zixing, Z., Nicholas, C., Zhao, R., et al. (2019b). Exploring deep spectrum representations via attention-based recurrent and convolutional neural networks for speech emotion recognition. IEEE Access, 7, 97515\u201397525.","journal-title":"IEEE Access"}],"container-title":["International Journal of Speech Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-020-09776-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10772-020-09776-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-020-09776-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T15:29:07Z","timestamp":1612970947000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10772-020-09776-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,24]]},"references-count":18,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["9776"],"URL":"https:\/\/doi.org\/10.1007\/s10772-020-09776-x","relation":{},"ISSN":["1381-2416","1572-8110"],"issn-type":[{"value":"1381-2416","type":"print"},{"value":"1572-8110","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,24]]},"assertion":[{"value":"8 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}