{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:26:08Z","timestamp":1775323568726,"version":"3.50.1"},"reference-count":17,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2025,4,1]]},"DOI":"10.1587\/transinf.2024edl8054","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T22:16:01Z","timestamp":1730412961000},"page":"406-410","source":"Crossref","is-referenced-by-count":1,"title":["Joint Distribution-Aligned Dual-Sparse Linear Regression for Cross-Stimulus Speech-Based Depression Detection"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Yingying","family":"LU","sequence":"first","affiliation":[{"name":"Psychological Health Counseling Center, Nanjing Tech University Pujiang Institute"}]},{"given":"Cheng","family":"LU","sequence":"additional","affiliation":[{"name":"School of Biological Science and Medical Engineering, Southeast University"}]},{"given":"Yuan","family":"ZONG","sequence":"additional","affiliation":[{"name":"School of Biological Science and Medical Engineering, Southeast University"}]},{"given":"Feng","family":"ZHOU","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yancheng Institute of Technology"}]},{"given":"Chuangao","family":"TANG","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Nanjing Institute of Technology"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] N. Cummins, S. Scherer, J. Krajewski, S. Schnieder, J. Epps, and T.F. Quatieri, \u201cA review of depression and suicide risk assessment using speech analysis,\u201d Speech communication, vol.71, pp.10-49, 2015. 10.1016\/j.specom.2015.03.004","DOI":"10.1016\/j.specom.2015.03.004"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] M. Hamilton, \u201cA rating scale for depression,\u201d Journal of neurology, neurosurgery, and psychiatry, vol.23, no.1, p.56, 1960. 10.1136\/jnnp.23.1.56","DOI":"10.1136\/jnnp.23.1.56"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] L. He, M. Niu, P. Tiwari, P. Marttinen, R. Su, J. Jiang, C. Guo, H. Wang, S. Ding, Z. Wang, X. Pan, and W. Dang, \u201cDeep learning for depression recognition with audiovisual cues: A review,\u201d Information Fusion, vol.80, pp.56-86, 2022. 10.1016\/j.inffus.2021.10.012","DOI":"10.1016\/j.inffus.2021.10.012"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] V. Mitra, E. Shriberg, D. Vergyri, B. Knoth, and R.M. Salomon, \u201cCross-corpus depression prediction from speech,\u201d 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4769-4773, IEEE, 2015. 10.1109\/icassp.2015.7178876","DOI":"10.1109\/ICASSP.2015.7178876"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Z. Huang, J. Epps, D. Joachim, B. Stasak, J.R. Williamson, and T.F. Quatieri, \u201cDomain adaptation for enhancing speech-based depression detection in natural environmental conditions using dilated cnns,\u201d INTERSPEECH, pp.4561-4565, 2020. 10.21437\/interspeech.2020-3135","DOI":"10.21437\/Interspeech.2020-3135"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] M. Long, J. Wang, G. Ding, J. Sun, and P.S. Yu, \u201cTransfer feature learning with joint distribution adaptation,\u201d Proc. IEEE international conference on computer vision, pp.2200-2207, 2013. 10.1109\/iccv.2013.274","DOI":"10.1109\/ICCV.2013.274"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] H. Cai, Z. Yuan, Y. Gao, S. Sun, N. Li, F. Tian, H. Xiao, J. Li, Z. Yang, X. Li, Q. Zhao, Z. Liu, Z. Yao, M. Yang, H. Peng, J. Zhu, X. Zhang, G. Gao, F. Zheng, R. Li, Z. Guo, R. Ma, J. Yang, L. Zhang, X. Hu, Y. Li, and B. Hu, \u201cA multi-modal open dataset for mental-disorder analysis,\u201d Scientific Data, vol.9, no.1, p.178, 2022. 10.1038\/s41597-022-01211-x","DOI":"10.1038\/s41597-022-01211-x"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] B. Schuller, S. Steidl, and A. Batliner, \u201cThe interspeech 2009 emotion challenge,\u201d Interspeech 2009, 2009. 10.21437\/interspeech.2009-103","DOI":"10.21437\/Interspeech.2009-103"},{"key":"9","unstructured":"[9] Z. Lin, R. Liu, and Z. Su, \u201cLinearized alternating direction method with adaptive penalty for low-rank representation,\u201d Advances in Neural Information Processing Systems, vol.24, 2011."},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] W. Zheng, M. Xin, X. Wang, and B. Wang, \u201cA novel speech emotion recognition method via incomplete sparse least square regression,\u201d IEEE Signal Process. Lett., vol.21, no.5, pp.569-572, 2014. 10.1109\/lsp.2014.2308954","DOI":"10.1109\/LSP.2014.2308954"},{"key":"11","unstructured":"[11] J. Liu, S. Ji, J. Ye, et al., \u201cSlep: Sparse learning with efficient projections,\u201d Arizona State University, vol.6, no.491, p.7, 2009."},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] S.J. Pan, I.W. Tsang, J.T. Kwok, and Q. Yang, \u201cDomain adaptation via transfer component analysis,\u201d IEEE Trans. Neural Netw., vol.22, no.2, pp.199-210, 2010. 10.1109\/tnn.2010.2091281","DOI":"10.1109\/TNN.2010.2091281"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] B. Gong, Y. Shi, F. Sha, and K. Grauman, \u201cGeodesic flow kernel for unsupervised domain adaptation,\u201d 2012 IEEE conference on computer vision and pattern recognition, pp.2066-2073, IEEE, 2012. 10.1109\/cvpr.2012.6247911","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, \u201cUnsupervised visual domain adaptation using subspace alignment,\u201d Proc. IEEE international conference on computer vision, pp.2960-2967, 2013. 10.1109\/iccv.2013.368","DOI":"10.1109\/ICCV.2013.368"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] M. Long, J. Wang, J. Sun, and P.S. Yu, \u201cDomain invariant transfer kernel learning,\u201d IEEE Trans. Knowl. Data Eng., vol.27, no.6, pp.1519-1532, 2014. 10.1109\/tkde.2014.2373376","DOI":"10.1109\/TKDE.2014.2373376"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] N. Liu, Y. Zong, B. Zhang, L. Liu, J. Chen, G. Zhao, and J. Zhu, \u201cUnsupervised cross-corpus speech emotion recognition using domain-adaptive subspace learning,\u201d 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5144-5148, IEEE, 2018. 10.1109\/icassp.2018.8461848","DOI":"10.1109\/ICASSP.2018.8461848"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. Zhang, L. Jiang, Y. Zong, W. Zheng, and L. Zhao, \u201cCross-corpus speech emotion recognition using joint distribution adaptive regression,\u201d ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3790-3794, IEEE, 2021. 10.1109\/icassp39728.2021.9414372","DOI":"10.1109\/ICASSP39728.2021.9414372"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/4\/E108.D_2024EDL8054\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T03:22:49Z","timestamp":1743823369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/4\/E108.D_2024EDL8054\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,1]]},"references-count":17,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edl8054","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,1]]},"article-number":"2024EDL8054"}}