{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:12:36Z","timestamp":1743052356563,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811651878"},{"type":"electronic","value":"9789811651885"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-5188-5_15","type":"book-chapter","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T23:04:52Z","timestamp":1629414292000},"page":"197-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross Languages One-Versus-All Speech Emotion Classifier"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1424-2951","authenticated-orcid":false,"given":"Xiangrui","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6493-561X","authenticated-orcid":false,"given":"Junchi","family":"Bin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1284-1993","authenticated-orcid":false,"given":"Huakang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"issue":"4","key":"15_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433\u2013459 (2010). https:\/\/doi.org\/10.1002\/wics.101","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.specom.2019.12.001","volume":"116","author":"MB Ak\u00e7ay","year":"2020","unstructured":"Ak\u00e7ay, M.B., O\u011fuz, K.: Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 116, 56\u201376 (2020). https:\/\/doi.org\/10.1016\/j.specom.2019.12.001","journal-title":"Speech Commun."},{"key":"15_CR3","doi-asserted-by":"publisher","unstructured":"Badshah, A.M., Ahmad, J., Rahim, N., Baik, S.W.: Speech emotion recognition from spectrograms with deep convolutional neural network. In: 2017 International Conference on Platform Technology and Service, PlatCon 2017 - Proceedings, pp. 3\u20137 (2017). https:\/\/doi.org\/10.1109\/PlatCon.2017.7883728","DOI":"10.1109\/PlatCon.2017.7883728"},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.bspc.2017.03.016","volume":"36","author":"SZ Bong","year":"2017","unstructured":"Bong, S.Z., Wan, K., Murugappan, M., Ibrahim, N.M., Rajamanickam, Y., Mohamad, K.: Implementation of wavelet packet transform and non linear analysis for emotion classification in stroke patient using brain signals. Biomed. Signal Process. Control 36, 102\u2013112 (2017). https:\/\/doi.org\/10.1016\/j.bspc.2017.03.016","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"15_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique nitesh. J. Artif. Intell. Res. 16(2), 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"Chen, M., Zhao, X.: A multi-scale fusion framework for bimodal speech emotion recognition. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020-October, pp. 374\u2013378 (2020). https:\/\/doi.org\/10.21437\/Interspeech.2020-3156","DOI":"10.21437\/Interspeech.2020-3156"},{"key":"15_CR7","doi-asserted-by":"publisher","unstructured":"Chiba, Y., Nose, T., Ito, A.: Multi-stream attention-based BLSTM with feature segmentation for speech emotion recognition. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020-October, pp. 3301\u20133305 (2020). https:\/\/doi.org\/10.21437\/Interspeech.2020-1199","DOI":"10.21437\/Interspeech.2020-1199"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Deng, J., Zhang, Z., Marchi, E., Schuller, B.: Sparse autoencoder-based feature transfer learning for speech emotion recognition. In: Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013, pp. 511\u2013516 (2013). https:\/\/doi.org\/10.1109\/ACII.2013.90","DOI":"10.1109\/ACII.2013.90"},{"issue":"3","key":"15_CR9","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.: Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recogn. 44(3), 572\u2013587 (2011). https:\/\/doi.org\/10.1016\/j.patcog.2010.09.020","journal-title":"Pattern Recogn."},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"Feng, H., Ueno, S., Kawahara, T.: End-to-end speech emotion recognition combined with acoustic-to-word ASR model. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020-October, pp. 501\u2013505 (2020). https:\/\/doi.org\/10.21437\/Interspeech.2020-1180","DOI":"10.21437\/Interspeech.2020-1180"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Fujioka, T., Homma, T., Nagamatsu, K.: Meta-learning for speech emotion recognition considering ambiguity of emotion labels. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020-October, pp. 2332\u20132336 (2020). https:\/\/doi.org\/10.21437\/Interspeech.2020-1082","DOI":"10.21437\/Interspeech.2020-1082"},{"issue":"4","key":"15_CR12","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1016\/j.jnca.2006.09.007","volume":"30","author":"H Gunes","year":"2007","unstructured":"Gunes, H., Piccardi, M.: Bi-modal emotion recognition from expressive face and body gestures. J. Netw. Comput. Appl. 30(4), 1334\u20131345 (2007). https:\/\/doi.org\/10.1016\/j.jnca.2006.09.007","journal-title":"J. Netw. Comput. Appl."},{"key":"15_CR13","first-page":"1957","volume":"11","author":"A Ilin","year":"2010","unstructured":"Ilin, A., Raiko, T.: Practical approaches to principal component analysis in the presence of missing values. J. Mach. Learn. Res. 11, 1957\u20132000 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"101894","DOI":"10.1016\/j.bspc.2020.101894","volume":"59","author":"D Issa","year":"2020","unstructured":"Issa, D., Demirci, M.F., Yazici, A.: Speech emotion recognition with deep convolutional neural networks. Biomed. Signal Process. Control 59, 101894 (2020). https:\/\/doi.org\/10.1016\/j.bspc.2020.101894","journal-title":"Biomed. Signal Process. Control"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Latif, S., Asim, M., Rana, R., Khalifa, S., Jurdak, R., Schuller, B.W.: Augmenting Generative Adversarial Networks for Speech Emotion Recognition. arXiv, pp. 521\u2013525 (2020)","DOI":"10.21437\/Interspeech.2020-3194"},{"issue":"6","key":"15_CR16","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s12652-016-0406-z","volume":"8","author":"Y Li","year":"2017","unstructured":"Li, Y., Tao, J., Chao, L., Bao, W., Liu, Y.: CHEAVD: a Chinese natural emotional audio-visual database. J. Ambient Intell. Humanized Comput. 8(6), 913\u2013924 (2017). https:\/\/doi.org\/10.1007\/s12652-016-0406-z","journal-title":"J. Ambient Intell. Humanized Comput."},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Tao, J., Technology, I., Jiang, D., Shan, S., Jia, J.: MEC 2017: Multimodal Emotion Recognition Challenge (2018)","DOI":"10.1109\/ACIIAsia.2018.8470342"},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Lim, W., Jang, D., Lee, T.: Speech emotion recognition using convolutional and recurrent neural networks. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, pp. 3\u20136 (2017). https:\/\/doi.org\/10.1109\/APSIPA.2016.7820699","DOI":"10.1109\/APSIPA.2016.7820699"},{"issue":"3","key":"15_CR19","doi-asserted-by":"publisher","first-page":"185","DOI":"10.2190\/DUGG-P24E-52WK-6CDG","volume":"9","author":"JD Mayer","year":"1989","unstructured":"Mayer, J.D.: Emotional intelligence. Imagination Cogn. Pers. 9(3), 185\u2013211 (1989). https:\/\/doi.org\/10.2190\/DUGG-P24E-52WK-6CDG","journal-title":"Imagination Cogn. Pers."},{"issue":"4","key":"15_CR20","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/TAFFC.2015.2432810","volume":"6","author":"M Nardelli","year":"2015","unstructured":"Nardelli, M., Valenza, G., Greco, A., Lanata, A., Scilingo, E.P.: Recognizing emotions induced by affective sounds through heart rate variability. IEEE Trans. Affect. Comput. 6(4), 385\u2013394 (2015). https:\/\/doi.org\/10.1109\/TAFFC.2015.2432810","journal-title":"IEEE Trans. Affect. Comput."},{"key":"15_CR21","doi-asserted-by":"publisher","unstructured":"Niu, Y., Zou, D., Niu, Y., He, Z., Tan, H.: Improvement on speech emotionrecognition based on deep convolutional neural networks.pdf (2018). https:\/\/doi.org\/10.1145\/3194452.3194460","DOI":"10.1145\/3194452.3194460"},{"issue":"4","key":"15_CR22","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/S0167-6393(03)00099-2","volume":"41","author":"TL Nwe","year":"2003","unstructured":"Nwe, T.L., Foo, S.W., De Silva, L.C.: Speech emotion recognition using hidden Markov models. Speech Commun. 41(4), 603\u2013623 (2003). https:\/\/doi.org\/10.1016\/S0167-6393(03)00099-2","journal-title":"Speech Commun."},{"key":"15_CR23","doi-asserted-by":"publisher","unstructured":"Schuller, B., Rigoll, G., Lang, M.: Hidden Markov model-based speech emotion recognition. In: Proceedings - IEEE International Conference on Multimedia and Expo 1, pp. I401\u2013I404 (2003). https:\/\/doi.org\/10.1109\/ICME.2003.1220939","DOI":"10.1109\/ICME.2003.1220939"},{"key":"15_CR24","doi-asserted-by":"publisher","unstructured":"Shen, G., et al.: WISE: word-level interaction-based multimodal fusion for speech emotion recognition. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020-October, pp. 369\u2013373 (2020). https:\/\/doi.org\/10.21437\/Interspeech.2020-3131","DOI":"10.21437\/Interspeech.2020-3131"},{"key":"15_CR25","doi-asserted-by":"publisher","unstructured":"Su, B.H., Chang, C.M., Lin, Y.S., Lee, C.C.: Improving speech emotion recognition using graph attentive Bi-directional gated recurrent unit network. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020-October, pp. 506\u2013510 (2020). https:\/\/doi.org\/10.21437\/Interspeech","DOI":"10.21437\/Interspeech"},{"key":"15_CR26","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.bspc.2014.10.008","volume":"18","author":"Y Sun","year":"2015","unstructured":"Sun, Y., Wen, G., Wang, J.: Weighted spectral features based on local Hu moments for speech emotion recognition. Biomed. Signal Process. Control 18, 80\u201390 (2015). https:\/\/doi.org\/10.1016\/j.bspc.2014.10.008","journal-title":"Biomed. Signal Process. Control"},{"key":"15_CR27","unstructured":"Theodoros, G.: A Python library for audio feature extraction, classification, segmentation and applications. https:\/\/github.com\/tyiannak\/pyAudioAnalysis"},{"key":"15_CR28","doi-asserted-by":"publisher","unstructured":"Trigeorgis, G., et al.: Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2016-May, pp. 5200\u20135204 (2016). https:\/\/doi.org\/10.1109\/ICASSP.2016.7472669","DOI":"10.1109\/ICASSP.2016.7472669"},{"issue":"1","key":"15_CR29","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.bspc.2014.07.005","volume":"14","author":"R Yuvaraj","year":"2014","unstructured":"Yuvaraj, R., et al.: Detection of emotions in Parkinson\u2019s disease using higher order spectral features from brain\u2019s electrical activity. Biomed. Signal Process. Control 14(1), 108\u2013116 (2014). https:\/\/doi.org\/10.1016\/j.bspc.2014.07.005","journal-title":"Biomed. Signal Process. Control"},{"key":"15_CR30","doi-asserted-by":"publisher","unstructured":"Zhang, X., Xu, M., Zheng, T.F.: Ensemble system for multimodal emotion recognition challenge. In: 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018 (MEC 2017), pp. 7\u201312 (2018). https:\/\/doi.org\/10.1109\/ACIIAsia.2018.8470352","DOI":"10.1109\/ACIIAsia.2018.8470352"},{"key":"15_CR31","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.patcog.2017.07.024","volume":"72","author":"T Zhu","year":"2017","unstructured":"Zhu, T., Lin, Y., Liu, Y.: Synthetic minority oversampling technique for multiclass imbalance problems. Pattern Recogn. 72, 327\u2013340 (2017). https:\/\/doi.org\/10.1016\/j.patcog.2017.07.024","journal-title":"Pattern Recogn."}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-5188-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T23:10:29Z","timestamp":1629414629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-5188-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811651878","9789811651885"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-5188-5_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"20 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dl2link.com\/ncaa2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"144","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"54","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.07","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.62","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}