{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:15:51Z","timestamp":1778602551052,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural National Science Foundation of China (NSFC)","award":["U2003207"],"award-info":[{"award-number":["U2003207"]}]},{"name":"Natural National Science Foundation of China (NSFC)","award":["61902064"],"award-info":[{"award-number":["61902064"]}]},{"name":"Natural National Science Foundation of China (NSFC)","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}]},{"name":"Natural National Science Foundation of China (NSFC)","award":["YBPY1955"],"award-info":[{"award-number":["YBPY1955"]}]},{"name":"Jiangsu Frontier Technology Basic Research Project","award":["U2003207"],"award-info":[{"award-number":["U2003207"]}]},{"name":"Jiangsu Frontier Technology Basic Research Project","award":["61902064"],"award-info":[{"award-number":["61902064"]}]},{"name":"Jiangsu Frontier Technology Basic Research Project","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}]},{"name":"Jiangsu Frontier Technology Basic Research Project","award":["YBPY1955"],"award-info":[{"award-number":["YBPY1955"]}]},{"name":"Zhishan Young Scholarship of Southeast University","award":["U2003207"],"award-info":[{"award-number":["U2003207"]}]},{"name":"Zhishan Young Scholarship of Southeast University","award":["61902064"],"award-info":[{"award-number":["61902064"]}]},{"name":"Zhishan Young Scholarship of Southeast University","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}]},{"name":"Zhishan Young Scholarship of Southeast University","award":["YBPY1955"],"award-info":[{"award-number":["YBPY1955"]}]},{"name":"Scientific Research Foundation of Graduate School of Southeast University","award":["U2003207"],"award-info":[{"award-number":["U2003207"]}]},{"name":"Scientific Research Foundation of Graduate School of Southeast University","award":["61902064"],"award-info":[{"award-number":["61902064"]}]},{"name":"Scientific Research Foundation of Graduate School of Southeast University","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}]},{"name":"Scientific Research Foundation of Graduate School of Southeast University","award":["YBPY1955"],"award-info":[{"award-number":["YBPY1955"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Cross-corpus speech emotion recognition (SER) is a challenging task, and its difficulty lies in the mismatch between the feature distributions of the training (source domain) and testing (target domain) data, leading to the performance degradation when the model deals with new domain data. Previous works explore utilizing domain adaptation (DA) to eliminate the domain shift between the source and target domains and have achieved the promising performance in SER. However, these methods mainly treat cross-corpus tasks simply as the DA problem, directly aligning the distributions across domains in a common feature space. In this case, excessively narrowing the domain distance will impair the emotion discrimination of speech features since it is difficult to maintain the completeness of the emotion space only by an emotion classifier. To overcome this issue, we propose a progressively discriminative transfer network (PDTN) for cross-corpus SER in this paper, which can enhance the emotion discrimination ability of speech features while eliminating the mismatch between the source and target corpora. In detail, we design two special losses in the feature layers of PDTN, i.e., emotion discriminant loss Ld and distribution alignment loss La. By incorporating prior knowledge of speech emotion into feature learning (i.e., high and low valence speech emotion features have their respective cluster centers), we integrate a valence-aware center loss Lv and an emotion-aware center loss Lc as the Ld to guarantee the discriminative learning of speech emotions except an emotion classifier. Furthermore, a multi-layer distribution alignment loss La is adopted to more precisely eliminate the discrepancy of feature distributions between the source and target domains. Finally, through the optimization of PDTN by combining three losses, i.e., cross-entropy loss Le, Ld, and La, we can gradually eliminate the domain mismatch between the source and target corpora while maintaining the emotion discrimination of speech features. Extensive experimental results of six cross-corpus tasks on three datasets, i.e., Emo-DB, eNTERFACE, and CASIA, reveal that our proposed PDTN outperforms the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/e24081046","type":"journal-article","created":{"date-parts":[[2022,7,31]],"date-time":"2022-07-31T23:37:29Z","timestamp":1659310649000},"page":"1046","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Progressively Discriminative Transfer Network for Cross-Corpus Speech Emotion Recognition"],"prefix":"10.3390","volume":"24","author":[{"given":"Cheng","family":"Lu","sequence":"first","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210096, China"},{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuangao","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210096, China"},{"name":"School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiacheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210096, China"},{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210096, China"},{"name":"School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/79.911197","article-title":"Emotion recognition in human-computer interaction","volume":"18","author":"Cowie","year":"2001","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1109\/MSP.2021.3096415","article-title":"Intelligent signal processing for affective computing","volume":"38","author":"Schuller","year":"2021","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lu, C., Zheng, W., Li, C., Tang, C., Liu, S., Yan, S., and Zong, Y. (2018, January 16\u201320). Multiple spatio-temporal feature learning for video-based emotion recognition in the wild. Proceedings of the 20th ACM International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3264992"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, S., Zheng, W., Zong, Y., Lu, C., Tang, C., Jiang, X., Liu, J., and Xia, W. (2019, January 14\u201318). Bi-modality fusion for emotion recognition in the wild. Proceedings of the 2019 International Conference on Multimodal Interaction, Suzhou, China.","DOI":"10.1145\/3340555.3355719"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG emotion recognition using dynamical graph convolutional neural networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TAFFC.2017.2705696","article-title":"Transfer Linear Subspace Learning for Cross-Corpus Speech Emotion Recognition","volume":"10","author":"Song","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shami, M., and Verhelst, W. (2007). Automatic classification of expressiveness in speech: A multi-corpus study. Speaker Classification II, Springer.","DOI":"10.1007\/978-3-540-74122-0_5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/T-AFFC.2010.8","article-title":"Cross-corpus acoustic emotion recognition: Variances and strategies","volume":"1","author":"Schuller","year":"2010","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1109\/LSP.2016.2537926","article-title":"Cross-corpus speech emotion recognition based on domain-adaptive least-squares regression","volume":"23","author":"Zong","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.specom.2016.07.010","article-title":"Cross-corpus speech emotion recognition based on transfer non-negative matrix factorization","volume":"83","author":"Song","year":"2016","journal-title":"Speech Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1109\/TAFFC.2018.2800046","article-title":"Feature selection based transfer subspace learning for speech emotion recognition","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, J., Jiang, L., Zong, Y., Zheng, W., and Zhao, L. (2021, January 6\u201311). Cross-Corpus Speech Emotion Recognition Using Joint Distribution Adaptive Regression. Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414372"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.1109\/TASLP.2018.2867099","article-title":"Domain adversarial for acoustic emotion recognition","volume":"26","author":"Abdelwahab","year":"2018","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1109\/TAFFC.2019.2916092","article-title":"Improving cross-corpus speech emotion recognition with adversarial discriminative domain generalization (ADDoG)","volume":"12","author":"Gideon","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/TASLP.2022.3178232","article-title":"Domain Invariant Feature Learning for Speaker-Independent Speech Emotion Recognition","volume":"30","author":"Lu","year":"2022","journal-title":"IEEE\/ACM Trans. Audio, Speech, Lang. Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1109\/TASL.2013.2255278","article-title":"On acoustic emotion recognition: Compensating for covariate shift","volume":"21","author":"Hassan","year":"2013","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.specom.2017.06.006","article-title":"Learning emotion-discriminative and domain-invariant features for domain adaptation in speech emotion recognition","volume":"93","author":"Mao","year":"2017","journal-title":"Speech Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1109\/TMM.2017.2766843","article-title":"Speech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching","volume":"20","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Plutchik, R. (1980). A general psychoevolutionary theory of emotion. Theories of Emotion, Elsevier.","DOI":"10.1016\/B978-0-12-558701-3.50007-7"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_22","unstructured":"Yang, L., Shen, Y., Mao, Y., and Cai, L. (2021). Hybrid Curriculum Learning for Emotion Recognition in Conversation. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rakshit, S., Banerjee, B., Roig, G., and Chaudhuri, S. (2019). Unsupervised multi-source domain adaptation driven by deep adversarial ensemble learning. German Conference on Pattern Recognition, Springer.","DOI":"10.1007\/978-3-030-33676-9_34"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sun, B., and Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref_25","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How transferable are features in deep neural networks?. Proceedings of the Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, QC, USA."},{"key":"ref_26","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics\u2014JMLR Workshop and Conference Proceedings, Fort Lauderdale, FL, USA."},{"key":"ref_27","unstructured":"Long, M., Zhu, H., Wang, J., and Jordan, M.I. (2017, January 6\u201311). Deep transfer learning with joint adaptation networks. Proceedings of the International Conference on Machine Learning (PMLR), Sydney, Australia."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Martin, O., Kotsia, I., Macq, B., and Pitas, I. (2006, January 3\u20137). The eNTERFACE\u201905 audio-visual emotion database. Proceedings of the 22nd IEEE International Conference on Data Engineering Workshops (ICDEW\u201906), Atlanta, GA, USA.","DOI":"10.1109\/ICDEW.2006.145"},{"key":"ref_29","unstructured":"Zhang, J.T.F.L.M., and Jia, H. (2008). Design of speech corpus for mandarin text to speech. The Blizzard Challenge 2008 Workshop, International Speech Communication Association."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W.F., and Weiss, B. (2005, January 4\u20138). A database of German emotional speech. Proceedings of the Interspeech, Lisbon, Portugal.","DOI":"10.21437\/Interspeech.2005-446"},{"key":"ref_31","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, NV, USA."},{"key":"ref_32","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_33","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TNNLS.2020.2988928","article-title":"Deep subdomain adaptation network for image classification","volume":"32","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_35","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1046\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:59:18Z","timestamp":1760140758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,29]]},"references-count":36,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["e24081046"],"URL":"https:\/\/doi.org\/10.3390\/e24081046","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,29]]}}}