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[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()
基于半监督判别分析的语音情感识别
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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
基于半监督判别分析的语音情感识别
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
徐新洲1;  黄程韦2;  金赟1;  吴尘1;  赵力1;  3
1东南大学水声信号处理教育部重点实验室, 南京 210096; 2苏州大学物理科学与技术学院, 苏州 215006; 3东南大学儿童发展与学习科学教育部重点实验室, 南京 210096
Keywords:
speech emotion recognition;  speech emotion feature;  semi-supervised discriminant analysis;  dimensionality reduction
语音情感识别;  语音情感特征;  半监督判别分析;  维数约简
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.
将基于多个嵌入图组合形式的半监督判别分析(SDA)以及核SDA(KSDA)应用于全监督的语音情感识别. 在语音信号样本情感成分的预处理阶段, 从样本语段中提取出多种特征及其统计参数, 包括基音、过零率、能量、持续长度、共振峰和 MFCC(Mel频率倒谱系数). 在将样本特征送入分类器之前的维数约简阶段, 使用经过参数优化的SDA或KSDA进行降维. Berlin语音情感数据库上的实验表明, 在使用多类SVM分类器时的全监督语音情感识别中, SDA优于其他一些先进的基于谱图学习的维数约简算法, 如LDA, LPP, MFA等, 而KSDA通过核化的数据映射, 能够取得比上述所有算法更好的识别效果.

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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]
Last Update: 2014-03-20