Abstract
To deal with the problem of cross-domain speech emotion recognition (SER), in this paper, we propose a novel dynamic graph-guided transferable regression (DGTR) method. Specifically, a retargeted discriminant linear regression in the source domain is utilized to make the projection matrix discriminative. Meanwhile, an adaptive maximum entropy graph is designed for similarity measurement for different domains. Experiments on four popular datasets show that our method can achieve better performance compared with several related state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akçay, M.B., Oğuz, K.: Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 116, 56–76 (2020)
Song, P.: Transfer linear subspace learning for cross-corpus speech emotion recognition. IEEE Trans. Affect. Comput. 10(02), 265–275 (2019)
Zhang, L., Gao, X.: Transfer adaptation learning: a decade survey. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2010)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200–2207 (2013)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1417 (2014)
Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 1129–1134. IEEE (2017)
Zhang, Y., Li, W., Tao, R., Peng, J., Du, Q., Cai, Z.: Cross-scene hyperspectral image classification with discriminative cooperative alignment. IEEE Trans. Geosci. Remote Sens. 59(11), 9646–9660 (2021)
Li, S., Song, P., Zhao, K., Zhang, W., Zheng, W.: Coupled discriminant subspace alignment for cross-database speech emotion recognition. Proc. Interspeech 2022, 4695–4699 (2022)
Zhang, X.Y., Wang, L., Xiang, S., Liu, C.L.: Retargeted least squares regression algorithm. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 2206–2213 (2014)
Mohar, B., Alavi, Y., Chartrand, G., Oellermann, O.: The Laplacian spectrum of graphs. Graph Theory Comb. Appl. 2(871–898), 12 (1991)
Li, Z., Nie, F., Chang, X., Nie, L., Zhang, H., Yang, Y.: Rank-constrained spectral clustering with flexible embedding. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 6073–6082 (2018)
Fan, K.: On a theorem of Weyl concerning eigenvalues of linear transformations I. Proc. Natl. Acad. Sci. 35(11), 652–655 (1949)
Li, X., Zhang, H., Zhang, R., Liu, Y., Nie, F.: Generalized uncorrelated regression with adaptive graph for unsupervised feature selection. IEEE Trans. Neural Netw. Learn. Syst. 30(5), 1587–1595 (2018)
Wen, J., Zhong, Z., Zhang, Z., Fei, L., Lai, Z., Chen, R.: Adaptive locality preserving regression. IEEE Trans. Circuits Syst. Video Technol. 30(1), 75–88 (2018)
Li, S., Song, P., Zheng, W.: Multi-source discriminant subspace alignment for cross-domain speech emotion recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing (2023)
Song, P., Zheng, W.: Feature selection based transfer subspace learning for speech emotion recognition. IEEE Trans. Affect. Comput. 11(3), 373–382 (2018)
Yu, C., Wang, J., Chen, Y., Huang, M.: Transfer learning with dynamic adversarial adaptation network. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 778–786. IEEE (2019)
Zhu, Y., et al.: Multi-representation adaptation network for cross-domain image classification. Neural Netw. 119, 214–221 (2019)
Zhu, Y., et al.: Deep subdomain adaptation network for image classification. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1713–1722 (2020)
Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., Tian, Q.: Towards discriminability and diversity: batch nuclear-norm maximization under label insufficient situations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3941–3950 (2020)
Acknowledgment
This research was supported by the Natural Science Foundation of Shandong Province under Grants ZR2023MF063 and ZR2022MF314, and by the National Natural Science Foundation of China under Grant 61703360.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, S., Song, P., Wang, R., Li, S., Zheng, W. (2023). Dynamic Graph-Guided Transferable Regression for Cross-Domain Speech Emotion Recognition. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_22
Download citation
DOI: https://doi.org/10.1007/978-981-99-8565-4_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8564-7
Online ISBN: 978-981-99-8565-4
eBook Packages: Computer ScienceComputer Science (R0)