摘要
语音情感识别是人机交互的研究热点之一.针对传统随机森林模型(RF)中决策树不分优劣具有同样决策权的不合理性,提出一种差分进化加权优化的随机森林分类模型(DERF).RF中样本的选择及每个节点变量的产生都是随机的,因此每次分类结果会有微小波动.为提高系统分类稳定性及识别准确率,集成构建三个相同的DERF分类器,按照多数投票原则确定最终决策结果.实验中融合语音的时域特征、频域特征、听觉语谱图特征及非线性hurst参数特征,分别选取柏林数据库和CASIA中文库中的五种情感进行识别,结果表明,本文所提方法有效提高了系统识别性能.
Speech emotion recognition is one of the research hotspots of human-computer interaction.In view of the irrationality of the decision tree in the traditional random forest model(RF),which has the same decision-making power,a differential evolution weighted random forest classification model(DERF)is proposed.In RF,the sample and each node variable are generated randomly,so there will be slight fluctuations in each classification result.In order to improve the system classification stability and recognition accuracy,three identical DERF classifiers are integrated and constructed,and the final decision results are determined according to the majority voting principle.In the experiment,the time domain feature,auditory language spectrum feature and nonlinear hurst parameter feature of speech were combined.The five emotions in the Berlin database and the CASIA database were selected to identify the results.The results show that the proposed method improves the system recognition effectively.
作者
李高玲
帖云
齐林
LI Gao-ling;TIE Yun;QI Lin(School of Industrial Technology Research Institute,Zhengzhou University,Zhengzhou 450001,China)
出处
《微电子学与计算机》
北大核心
2019年第1期70-73,共4页
Microelectronics & Computer
关键词
语音情感识别
随机森林
差分进化
多数投票
speech emotion recognition
random forest
differential evolution
majority voting
