[1] Xu Xinzhou, Huang Chengwei, Jin Yun, Wu Chen, et al. Speech emotion recognitionusing semi-supervised discriminant analysis [J]. Journal of Southeast University (English Edition), 2014, 30 (1): 7-12. [doi:10.3969/j.issn.1003-7985.2014.01.002]

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Speech emotion recognitionusing semi-supervised discriminant analysis()

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

Volumn:

30

Issue:

2014 1

Page:

7-12

Research Field:

Information and Communication Engineering

Publishing date:

2014-03-31

Info

Title:

Speech emotion recognitionusing semi-supervised discriminant analysis

Author(s):

Xu Xinzhou¹;  Huang Chengwei²;  Jin Yun¹;  Wu Chen¹;  Zhao Li¹;  3

¹Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China
²School of Physical Science and Technology, Soochow University, Suzhou 215006, China
³ Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China

Keywords:

speech emotion recognition;  speech emotion feature;  semi-supervised discriminant analysis;  dimensionality reduction

PACS:

TN912.3

DOI:

10.3969/j.issn.1003-7985.2014.01.002

Abstract:

Semi-supervised discriminant analysis(SDA), which uses a combination of multiple embedding graphs, and kernel SDA(KSDA)are adopted in supervised speech emotion recognition. When the emotional factors of speech signal samples are preprocessed, different categories of features including pitch, zero-cross rate, energy, durance, formant and Mel frequency cepstrum coefficient(MFCC), as well as their statistical parameters, are extracted from the utterances of samples. In the dimensionality reduction stage before the feature vectors are sent into classifiers, parameter-optimized SDA and KSDA are performed to reduce dimensionality. Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning, such as linear discriminant analysis(LDA), locality preserving projections(LPP), marginal Fisher analysis(MFA)etc., when multi-class support vector machine(SVM)classifiers are used. Additionally, KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.

References:

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Memo

Memo:

Biographies: Xu Xinzhou(1987—), male, graduate; Zhao Li(corresponding author), male, doctor, professor, zhaoli@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 61231002, 61273266), the Ph.D. Programs Foundation of Ministry of Education of China(No.20110092130004).
Citation: Xu Xinzhou, Huang Chengwei, Jin Yun, et al.Speech emotion recognition using semi-supervised discriminant analysis[J].Journal of Southeast University(English Edition), 2014, 30(1):7-12.[doi:10.3969/j.issn.1003-7985.2014.01.002]

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