[1] Jin Yun, Song Peng, Zheng Wenming, et al. Novel feature fusion method for speech emotion recognitionbased on multiple kernel learning [J]. Journal of Southeast University (English Edition), 2013, 29 (2): 129-133. [doi:10.3969/j.issn.1003-7985.2013.02.004]

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Novel feature fusion method for speech emotion recognitionbased on multiple kernel learning()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 2

Page:

129-133

Research Field:

Information and Communication Engineering

Publishing date:

2013-06-20

Info

Title:

Novel feature fusion method for speech emotion recognitionbased on multiple kernel learning

Author(s):

Jin Yun¹;  2;  Song Peng¹;  Zheng Wenming³;  Zhao Li¹

¹School of Information Science and Engineering, Southeast University, Nanjing 210096, China
²School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou 221116, China
³Research Center for Learning Science, Southeast University, Nanjing 210096, China

Keywords:

speech emotion recognition;  multiple kernel learning;  feature fusion;  support vector machine

PACS:

TN912.3

DOI:

10.3969/j.issn.1003-7985.2013.02.004

Abstract:

In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. Based on the global features, the local information of different kinds of features is utilized. Both the global and the local features are combined together. Moreover, the multiple kernel learning method is adopted. The global features and each kind of local feature are respectively associated with a kernel, and all these kernels are added together with different weights to obtain a mixed kernel for nonlinear mapping. In the reproducing kernel Hilbert space, different kinds of emotional features can be easily classified. In the experiments, the popular Berlin dataset is used, and the optimal parameters of the global and the local kernels are determined by cross-validation. After computing using multiple kernel learning, the weights of all the kernels are obtained, which shows that the formant and intensity features play a key role in speech emotion recognition. The classification results show that the recognition rate is 78.74% by using the global kernel, and it is 81.10% by using the proposed method, which demonstrates the effectiveness of the proposed method.

References:

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[3] Zeng Z, Pantic M, Roisman G I, et al. A survey of affect recognition methods: audio, visual, and spontaneous expressions [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1):39-58.
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[9] Lin Yen-Yu, Liu Tyng-Luh, Fuh Chiou-Shann. Multiple kernel learning for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Learning, 2011, 33(6):1147-1160.
[10] Burkhardt F, Paeschke A, Rolfes M, et al. A database of German emotional speech [C]//9th European Conference on Speech Communication and Technology. Lisbon, Portugal, 2005:1517-1520.
[11] Bitouk D, Verma R, Nenkova A. Class-level spectral features for emotion recognition [J]. Speech Communication, 2010, 52(7/8):613-625.
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Memo

Memo:

Biographies: Jin Yun(1979—), male, graduate; Zhao Li(corresponding author), doctor, professor, zhaoli@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61231002, 61273266), the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
Citation: Jin Yun, Song Peng, Zheng Wenming, et al. Novel feature fusion method for speech emotion recognition based on multiple kernel learning[J].Journal of Southeast University(English Edition), 2013, 29(2):129-133.[doi:10.3969/j.issn.1003-7985.2013.02.004]

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