摘要
提出基于ELM的广义神经网络语音情感识别模型,对基于ELM的单隐层前馈神经网络模型,采用多点交叉和多点变异遗传算法对模型参数进行优化;对基于核函数ELM的广义单隐层前馈神经网络,采用网格搜索寻找模型最优参数组合.对TYUT和EMO-DB情感语音库三种情感(高兴、生气和中性)的识别结果表明,所建立的基于ELM的语音情感识别模型,在泛化性能和训练速度上均优于SVM模型.
In this paper, the generalized networks based on ELM are proposed as recognition models. According to the experimental analysis, in the single hidden layer feed-forward network based on ELM, the multi-point crossover and multi- mutation genetic algorithm is used to optimize the parameters of the model. In the generalized single hidden layer feed-forward network based on kernel function ELM, grid search is used to find the optimal parameter combination of the model. On the basic of recognition results of the three emotions called happiness, anger and which are drawn from the TYUT and EMO-DB emotional speech databases. The proposed speech emotion recognition models based on ELM are superior to SVM on both generalization performance and speed.
出处
《微电子学与计算机》
CSCD
北大核心
2015年第7期50-54,58,共6页
Microelectronics & Computer
基金
国家自然科学基金(61371193)
山西省青年科技研究基金(2013021016-2)
山西省回国留学人员科研项目(2013-034)
关键词
语音情感识别
极限学习机ELM
核函数ELM
支持向量机
speech emotion recognition
extreme learning machine
kernel function extreme learning machine
support vector machines
