{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:06:43Z","timestamp":1783098403888,"version":"3.54.6"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["U2003207"],"award-info":[{"award-number":["U2003207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["61902064"],"award-info":[{"award-number":["61902064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"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":"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"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, we focus on a challenging, but interesting, task in speech emotion recognition (SER), i.e., cross-corpus SER. Unlike conventional SER, a feature distribution mismatch may exist between the labeled source (training) and target (testing) speech samples in cross-corpus SER because they come from different speech emotion corpora, which degrades the performance of most well-performing SER methods. To address this issue, we propose a novel transfer subspace learning method called multiple distribution-adapted regression (MDAR) to bridge the gap between speech samples from different corpora. Specifically, MDAR aims to learn a projection matrix to build the relationship between the source speech features and emotion labels. A novel regularization term called multiple distribution adaption (MDA), consisting of a marginal and two conditional distribution-adapted operations, is designed to collaboratively enable such a discriminative projection matrix to be applicable to the target speech samples, regardless of speech corpus variance. Consequently, by resorting to the learned projection matrix, we are able to predict the emotion labels of target speech samples when only the source label information is given. To evaluate the proposed MDAR method, extensive cross-corpus SER tasks based on three different speech emotion corpora, i.e., EmoDB, eNTERFACE, and CASIA, were designed. Experimental results showed that the proposed MDAR outperformed most recent state-of-the-art transfer subspace learning methods and even performed better than several well-performing deep transfer learning methods in dealing with cross-corpus SER tasks.<\/jats:p>","DOI":"10.3390\/e24091250","type":"journal-article","created":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T23:35:57Z","timestamp":1662420957000},"page":"1250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0839-8792","authenticated-orcid":false,"given":"Yuan","family":"Zong","sequence":"first","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China"},{"name":"School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailun","family":"Lian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China"},{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongli","family":"Chang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China"},{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China"},{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuangao","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China"},{"name":"School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.patcog.2010.09.020","article-title":"Survey on speech emotion recognition: Features, classification schemes, and databases","volume":"44","author":"Kamel","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1145\/3129340","article-title":"Speech emotion recognition: Two decades in a nutshell, benchmarks, and ongoing trends","volume":"61","author":"Schuller","year":"2018","journal-title":"Commun. 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