[1] Huang Chengwei, Wu Di, Zhang Xiaojun, Xiao Zhongzhe, et al. Cascaded projection of Gaussian mixture modelfor emotion recognition in speech and ECG signals [J]. Journal of Southeast University (English Edition), 2015, 31 (3): 320-326. [doi:10.3969/j.issn.1003-7985.2015.03.004]

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Cascaded projection of Gaussian mixture modelfor emotion recognition in speech and ECG signals()

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

Volumn:

31

Issue:

2015 3

Page:

320-326

Research Field:

Information and Communication Engineering

Publishing date:

2015-09-20

Info

Title:

Cascaded projection of Gaussian mixture modelfor emotion recognition in speech and ECG signals

Author(s):

Huang Chengwei¹;  Wu Di¹;  Zhang Xiaojun¹;  Xiao Zhongzhe¹;  Xu Yishen¹;  Ji Jingjing¹;  Tao Zhi¹;  Zhao Li²

¹College of Physics, Optoelectronics and Energy, Soochow University, Suzhou 215006, China
²School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

Gaussian mixture model;  emotion recognition;  sample adaptation;  emotion inducing

PACS:

TN912.3

DOI:

10.3969/j.issn.1003-7985.2015.03.004

Abstract:

A cascaded projection of the Gaussian mixture model algorithm is proposed. First, the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model. Each sub-classifier is based on different feature sets. The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability. Secondly, the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal. Emotional data including fidgetiness, happiness and sadness is collected by induction experiments. Finally, the emotion feature extraction method is discussed, including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature. The emotional feature reduction methods are studied, including principle component analysis, sequential forward selection, the Fisher discriminant ratio and maximal information coefficient. The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.

References:

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Memo

Memo:

Biographies: Huang Chengwei(1984—), male, doctor, associate professor, cwhuang@suda.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61231002, 61273266, 51075068, 61271359), Doctoral Fund of Ministry of Education of China(No.20110092130004).
Citation: Huang Chengwei, Wu Di, Zhang Xiaojun, et al.Cascaded projection of Gaussian mixture model for emotion recognition in speech and ECG signals[J].Journal of Southeast University(English Edition), 2015, 31(3):320-326.[doi:10.3969/j.issn.1003-7985.2015.03.004]

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