南京理工大学学报（自然科学版）2017，Vol.41Issue(2)：191-197,7.DOI:10.14177/j.cnki.32-1397n.2017.41.02.009
基于核典型相关分析和支持向量机的语音情感识别模型
Speech emotion recognition model based on kernel canonicalcorrelation analysis and support vector machine
摘要
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.关键词
语音情感识别/核典型相关分析/特征选择/情感分类器/支持向量机Key words
speech emotion recognition/kernel canonical correlation analysis/feature selection/emotion classifiers/support vector machine分类
信息技术与安全科学引用本文复制引用
张前进,王华东..基于核典型相关分析和支持向量机的语音情感识别模型[J].南京理工大学学报（自然科学版）,2017,41(2):191-197,7.基金项目
2016年安徽省高等学校自然科学研究重点项目(KJ2016A120) （KJ2016A120）
