{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:20:48Z","timestamp":1753600848072,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811692468"},{"type":"electronic","value":"9789811692475"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-981-16-9247-5_1","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:25:33Z","timestamp":1641936333000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["WeaveNet: End-to-End Audiovisual Sentiment Analysis"],"prefix":"10.1007","author":[{"given":"Yinfeng","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhong","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiling","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuhong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","volume":"42","author":"C Busso","year":"2008","unstructured":"Busso, C., et al.: Iemocap: interactive emotional dyadic motion capture database. Lang. Resour. Eval. 42, 335\u2013359 (2008)","journal-title":"Lang. Resour. Eval."},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/MIS.2016.31","volume":"31","author":"E Cambria","year":"2016","unstructured":"Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31, 102\u2013107 (2016)","journal-title":"IEEE Intell. Syst."},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Chen, M., Wang, S., Liang, P.P., Baltrusaitis, T., Zadeh, A., Morency, L.P.: Multimodal sentiment analysis with word-level fusion and reinforcement learning. In: ICMI (2017)","DOI":"10.1145\/3136755.3136801"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Etienne, C., Fidanza, G., Petrovskii, A., Devillers, L., Schmauch, B.: Speech emotion recognition with data augmentation and layer-wise learning rate adjustment. CoRR abs\/1802.05630 (2018)","DOI":"10.21437\/SMM.2018-5"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Gievska, S., Koroveshovski, K., Tagasovska, N.: Bimodal feature-based fusion for real-time emotion recognition in a mobile context. In: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 401\u2013407 (2015)","DOI":"10.1109\/ACII.2015.7344602"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"G\u00fc\u00e7l\u00fct\u00fcrk, Y., G\u00fc\u00e7l\u00fc, U., van Gerven, M., van Lier, R.: Deep impression: audiovisual deep residual networks for multimodal apparent personality trait recognition. In: ECCV Workshops (2016)","DOI":"10.1007\/978-3-319-49409-8_28"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Hazarika, D., Poria, S., Mihalcea, R., Cambria, E., Zimmermann, R.: Icon: Interactive conversational memory network for multimodal emotion detection. In: EMNLP (2018)","DOI":"10.18653\/v1\/D18-1280"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Kim, D.H., Lee, M.K., Choi, D.Y., Song, B.C.: Multi-modal emotion recognition using semi-supervised learning and multiple neural networks in the wild. In: ICMI (2017)","DOI":"10.1145\/3136755.3143005"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Kim, J., Englebienne, G., Truong, K.P., Evers, V.: Deep temporal models using identity skip-connections for speech emotion recognition. In: ACM Multimedia (2017)","DOI":"10.1145\/3123266.3123353"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Liang, P.P., Liu, Z., Zadeh, A., Morency, L.P.: Multimodal language analysis with recurrent multistage fusion. CoRR abs\/1808.03920 (2018)","DOI":"10.18653\/v1\/D18-1014"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Shen, Y., Lakshminarasimhan, V.B., Liang, P.P., Zadeh, A., Morency, L.P.: Efficient low-rank multimodal fusion with modality-specific factors. In: ACL (2018)","DOI":"10.18653\/v1\/P18-1209"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Ma, X., Yang, H., Chen, Q., Huang, D., Wang, Y.: Depaudionet: an efficient deep model for audio based depression classification. In: AVEC@ACM Multimedia (2016)","DOI":"10.1145\/2988257.2988267"},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.1109\/TCYB.2016.2549639","volume":"47","author":"K Mistry","year":"2017","unstructured":"Mistry, K., Zhang, L., Neoh, S.C., Lim, C.P., Fielding, B.: A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Trans. Cybern. 47, 1496\u20131509 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Nasir, M., Jati, A., Shivakumar, P.G., Chakravarthula, S.N., Georgiou, P.G.: Multimodal and multiresolution depression detection from speech and facial landmark features. In: AVEC@ACM Multimedia (2016)","DOI":"10.1145\/2988257.2988261"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Nguyen, D.L., Nguyen, K., Sridharan, S., Ghasemi, A., Dean, D., Fookes, C.: Deep spatio-temporal features for multimodal emotion recognition. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1215\u20131223 (2017)","DOI":"10.1109\/WACV.2017.140"},{"key":"1_CR16","unstructured":"P\u00e9rez-Rosas, V., Mihalcea, R., Morency, L.P.: Utterance-level multimodal sentiment analysis. In: ACL (2013)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Pham, H., Manzini, T., Liang, P.P., P\u00f3czos, B.: Seq2seq2sentiment: Multimodal sequence to sequence models for sentiment analysis. CoRR abs\/1807.03915 (2018)","DOI":"10.18653\/v1\/W18-3308"},{"key":"1_CR18","doi-asserted-by":"publisher","unstructured":"Poria, S., Cambria, E., Hazarika, D., Mazumder, N., Zadeh, A., Morency, L.P.: Multi-level multiple attentions for contextual multimodal sentiment analysis. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 1033\u20131038 (November 2017). https:\/\/doi.org\/10.1109\/ICDM.2017.134","DOI":"10.1109\/ICDM.2017.134"},{"key":"1_CR19","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.: A review of affective computing: from unimodal analysis to multimodal fusion. Inf. Fusion 37, 98\u2013125 (2017)","journal-title":"Inf. Fusion"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Poria, S., Chaturvedi, I., Cambria, E., Hussain, A.: Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 439\u2013448 (2016)","DOI":"10.1109\/ICDM.2016.0055"},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TAFFC.2016.2588488","volume":"9","author":"KP Seng","year":"2018","unstructured":"Seng, K.P., Ang, L.M., Ooi, C.S.: A combined rule-based & machine learning audio-visual emotion recognition approach. IEEE Trans. Affect. Comput. 9, 3\u201313 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Sivaprasad, S., Joshi, T., Agrawal, R., Pedanekar, N.: Multimodal continuous prediction of emotions in movies using long short-term memory networks. In: ICMR (2018)","DOI":"10.1145\/3206025.3206076"},{"key":"1_CR23","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.imavis.2017.08.003","volume":"65","author":"M Soleymani","year":"2017","unstructured":"Soleymani, M., Garc\u00eda, D., Jou, B., Schuller, B.W., Chang, S.F., Pantic, M.: A survey of multimodal sentiment analysis. Image Vis. Comput. 65, 3\u201314 (2017)","journal-title":"Image Vis. Comput."},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Trigeorgis, G., et al.: Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5200\u20135204 (2016)","DOI":"10.1109\/ICASSP.2016.7472669"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1109\/JSTSP.2017.2764438","volume":"11","author":"P Tzirakis","year":"2017","unstructured":"Tzirakis, P., Trigeorgis, G., Nicolaou, M.A., Schuller, B.W., Zafeiriou, S.: End-to-end multimodal emotion recognition using deep neural networks. IEEE J. Sel. Top. Sign. Proces. 11, 1301\u20131309 (2017)","journal-title":"IEEE J. Sel. Top. Sign. Proces."},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Tzirakis, P., Zhang, J., Schuller, B.W.: End-to-end speech emotion recognition using deep neural networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5089\u20135093 (2018)","DOI":"10.1109\/ICASSP.2018.8462677"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Wu, A., Huang, Y., Zhang, G.: Feature fusion methods for robust speech emotion recognition based on deep belief networks. In: ICNCC 2016 (2016)","DOI":"10.1145\/3033288.3033295"},{"key":"1_CR28","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1109\/TMM.2016.2557721","volume":"18","author":"J Yan","year":"2016","unstructured":"Yan, J., Zheng, W., Xu, Q., Lu, G., Li, H., Wang, B.: Sparse kernel reduced-rank regression for bimodal emotion recognition from facial expression and speech. IEEE Trans. Multimedia 18, 1319\u20131329 (2016)","journal-title":"IEEE Trans. Multimedia"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Yang, L., Jiang, D., Xia, X., Pei, E., Oveneke, M.C., Sahli, H.: Multimodal measurement of depression using deep learning models. In: AVEC@ACM Multimedia (2017)","DOI":"10.1145\/3133944.3133948"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Chen, M., Poria, S., Cambria, E., Morency, L.P.: Tensor fusion network for multimodal sentiment analysis. In: Empirical Methods in Natural Language Processing, EMNLP (2017)","DOI":"10.18653\/v1\/D17-1115"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Liang, P.P., Poria, S., Vij, P., Cambria, E., Morency, L.P.: Multi-attention recurrent network for human communication comprehension. CoRR abs\/1802.00923 (2018)","DOI":"10.1609\/aaai.v32i1.12024"},{"key":"1_CR32","unstructured":"Zadeh, A., Zellers, R., Pincus, E., Morency, L.P.: Mosi: Multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. CoRR abs\/1606.06259 (2016)"},{"key":"1_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis : a survey. Wiley Interdisc. Rew. Data Min. Knowl. Discov. 8, e1253 (2018)","DOI":"10.1002\/widm.1253"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Zhu, B., Zhou, W., Wang, Y., Wang, H., Cai, J.J.: End-to-end speech emotion recognition based on neural network. In: 2017 IEEE 17th International Conference on Communication Technology (ICCT), pp. 1634\u20131638 (2017)","DOI":"10.1109\/ICCT.2017.8359907"}],"container-title":["Communications in Computer and Information Science","Cognitive Systems and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-9247-5_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T16:53:18Z","timestamp":1674406398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-9247-5_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811692468","9789811692475"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-9247-5_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Systems and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Suzhou","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":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccsip2021.tsingzhan.com\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"105","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":"41","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":"39% - 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","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","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)"}}]}}