Abstract
Remarkable advances have achieved in speech emotion recognition (SER) with efficient and feasible models. These studies focus on the ability of the model itself. However, they ignore the potential distributed information of speech data. Actually, emotion speech is imbalanced due to the expression of human being. To overcome the imbalanced problems of speech data, the ongoing work furthers our previous study of the Boundary-Eliminated Pseudo-Inverse Linear Discriminant (BEPILD) model through introducing the information entropy that contributes to describing the distribution of the speech data. As a result, an Entropy-based Boundary-Eliminated Pseudo-Inverse Linear Discriminant model (EBEPILD) is proposed to generate more robust hyperplanes to tackle the speech data with high class uncertainty. The experiments conducted on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database with four emotion states show that the EBEPILD has outstanding performance compared with other algorithms.
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References
Ayadi, M.E., Kamel, M.S., Karray, F.: Survey on speech emotion recognition: features, classification schemes, and databases. Pattern. Recogn. 44, 542–587 (2011)
Busso, C., Bulut, M., Lee, C.C., Kazemzadeh, A., Mower, E., Kim, S.: Iemocap: interactive emotional dyadic motion capture database. Lang. Res. Eval. 42(4), 335–359 (2008)
Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)
Eyben, F., Wöllmer, M., Schuller, B.: OpenSmile: the Munich versatile and fast open-source audio feature extractor. In: ACM International Conference on Multimedia, pp. 1459–1462 (2010)
Fayek, H.M., Lech, M., Cavedon, L.: Evaluating deep learning architectures for speech emotion recognition. Neural Netw. 92, 60–68 (2017)
Hu, H., Xu, M.X., Wu, W.: GMM supervector based SVM with spectral features for speech emotion recognition. In: IEEE International Conference on Acoustics, pp. IV-413 - IV-416 (2007)
Li, L., Zhao, Y., Jiang, D., Zhang, Y., Wang, F., Gonzalez, I., et al.: Hybrid deep neural network-hidden markov model (DNN-HMM) based speech emotion recognition. In: Pun, T., Pelachaud, C., Sebe, N. (eds.) ACII 2013, vol. 7971, pp. 312–317. IEEE, Geneva (2013). https://doi.org/10.1109/ACII.2013.58
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Han, K., Yu, D., Tashev, I.: Speech emotion recognition using deep neural network and extreme learning machine. In: INTERSPEECH (2014)
Mirsamadi, S., Barsoum, E., Zhang, C.: Automatic speech emotion recognition using recurrent neural networks with local attention. In: IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), pp. 2227–2231. IEEE(2017)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(4), 379–423 (1948)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2014)
Schuller, B., Steidl, S., Batliner, A.: The Interspeech 2009 emotion challenge. In: INTERSPEECH 2009, Conference of the International Speech Communication Association, pp. 312–315 (2009)
Tian, T., Ji, W., Gao, D.Q.: Threshold optimization of pseudo-inverse linear discriminants based on overall accuracies. In: International Joint Conference on Neural Networks, pp. 1–6 (2015)
Zhu, Y.J., Wang, Z., Zha, H.Y., Gao, D.Q.: Boundary-eliminated pseudoinverse linear discriminant for imbalanced problems. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–14 (2017)
Acknowledgements
This work is supported by Natural Science Foundations of China under Grant No. 61672227, “Shuguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, and Action Plan for Innovation on Science and Technology Projects of Shanghai under Grant No. 16511101000.
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Li, D., Sun, L., Wang, Z., Zhang, J. (2018). Entropy Based Boundary-Eliminated Pseudo-Inverse Linear Discriminant for Speech Emotion Recognition. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_62
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