基于聚类PSO-LSSVM模型的PAD维度预测  

Forecast of PAD dimensions using clustering PSO-LSSVM model

作　　者：胡艳香 孙颖 张雪英 段淑斐 Hu Yanxiang;Sun Ying;Zhang Xueying;Duan Shufei(College of Information&Computer,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学信息与计算机学院,太原030024

出　　处：《计算机应用研究》2020年第4期994-998,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61371193);山西省青年基金资助项目(2013021016-2);山西省研究生教育创新项目(2018SY021);山西省应用研究青年基金资助项目(201601D202045);山西省回国留学人员科研资助项目(201925);山西省自然科学基金面上项目(201901D111096)。

摘　　要：针对PAD(愉悦度、激活度、优势度)预测精度问题,提出将最小二乘支持向量机(least squares support vector machine,LSSVM)经粒子群优化(particle swarm optimization,PSO)算法优化再与情感聚类分析结合的聚类PSO-LSSVM模型。对TYUT2.0和柏林语音库的三种情感语音提取情感特征,基于特征与标注的P、A、D对三种单一情感分别建立各类情感维度PSO-LSSVM模型以及对三种情感建立混合情感维度PSO-LSSVM模型;然后利用混合情感维度PSO-LSSVM模型预测P、A、D,并计算其与基本情感PAD的距离;最后将距离大于阈值的情感聚类为混合情感,将距离小于阈值的情感聚类为与其距离最近的情感,并利用对应情感的回归模型预测其P、A、D。研究显示,该模型对P、A、D的预测误差较LSSVM和PSO-LSSVM模型更小,且预测值与标注值的相关性更强,说明聚类PSO-LSSVM模型对P、A、D的预测更加可靠、准确。In view of the imprecision problem for PAD(pleasure,arousal,dominance)prediction,this paper proposed clustering PSO-LSSVM model combining LSSVM optimized by PSO and affective clustering analysis.Firstly,it selected three emotion spe-eches of TYUT2.0 emotional speech database and Berlin voice library,and extracted emotion features.It established single emotional dimension PSO-LSSVM models for three single emotion and the mixed emotion dimension PSO-LSSVM model for three emotions based on emotion features and P,A and D values.The method used mixed emotion dimension PSO-LSSVM model to predict the P,A and D values of the test set,and calculated the distance between the predictive PAD and the PAD of the basic emotion.Finally it clustered the emotion whose distance was greater than the threshold into mixed emotion,and clustered the emotion whose distance was less than the threshold into the nearest emotions,then used the corresponding emotional dimension regression model to predict its P,A and D.The research shows that the predictive error of clustering PSO-LSSVM regression model to P,A and D is smaller than that of LSSVM and PSO-LSSVM model,and the correlation between the predicted value and the tagged va-lue is stronger.So the clustering PSO-LSSVM regression model is more reliable and accurate in predicting P,A and D values.

关 键 词：情感维度PAD 最小二乘支持向量机 粒子群优化算法 情感聚类分析 

分 类 号：TN912.3[电子电信—通信与信息系统]