| 基于半监督判别分析的语音情感识别(英文) |
| |
| 引用本文： | 徐新洲,黄程韦,金赟,吴尘,赵力.基于半监督判别分析的语音情感识别(英文)[J].东南大学学报,2014(1):7-12. |
| |
| 作者姓名： | 徐新洲 黄程韦 金赟 吴尘 赵力 |
| |
| 作者单位： | [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 |
| |
| 基金项目： | Foundation items： The National Natural Science Foundation of China （No. 61231002, 61273266）, the Ph. D. Programs Foundation of Min- istry of Education of China （No. 20110092130004）. |
| |
| 摘 要： | 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.
|
| 关 键 词： | 语音情感识别 语音情感特征 半监督判别分析 维数约简 |
Speech emotion recognition using semi-supervised discriminant analysis |
| |
| Xu Xinzhou,Huang Chengwei,Jin Yun,Wu Chen,Zhao Li.Speech emotion recognition using semi-supervised discriminant analysis[J].Journal of Southeast University(English Edition),2014(1):7-12. |
| |
| Authors: | Xu Xinzhou Huang Chengwei Jin Yun Wu Chen Zhao Li |
| |
| Institution: | Xu Xinzhou, Huang Chengwei, Jin Yun, Wu Chen, Zhao Li |
| |
| 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. |
| |
| Keywords: | speech emotion recognition speech emotion feature semi-supervised discriminant analysis dimensionality reduction |
| 本文献已被 CNKI 维普 等数据库收录！ |
|
