|Table of Contents|

Speech emotion recognition model based on kernel canonicalcorrelation analysis and support vector machine(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年02期
Page:
191-
Research Field:
Publishing date:

Info

Title:
Speech emotion recognition model based on kernel canonicalcorrelation analysis and support vector machine
Author(s):
Zhang Qianjin1Wang Huadong2
1.Department of Information Engineering,Anhui Vocational College of Defense Technology,Lu’an 237011,China; 2.School of Computer Science and Technology,Zhoukou Normal University,Zhoukou 466001,China
Keywords:
speech emotion recognition kernel canonical correlation analysis feature selection emotion classifiers support vector machine
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2017.41.02.009
Abstract:
In order to obtain the better real-time and correct rate of the speech emotion recognition,an emotion recognition model based on the kernel canonical correlation analysis and the support vector machine is proposed here.Firstly,multiple features of the speech emotion recognition are extracted and the feature selection is selected by the kernel canonical correlation analysis,and then the selected features are taken as the input vector of the support vector machine to be trained for establishing the classifier of the speech emotion recognition.Finally,experiments on the standard database of the speech emotion recognition is used to validate the performance of the model.The experimental results show that,by using the kernel canonical correlation analysis with the less input vectors,the proposed model can accurately identify the emotion type and increase the recognition rate of the speech emotion,and has the better read-time.The result of the speech emotion recognition is better than that of the contrast models,and the model has the higher practical application value.

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Last Update: 2017-04-30