[1]韩志艳,王健. 多模式情感识别特征参数融合算法研究[J].计算机技术与发展,2016,26(05):27-30.
　HAN Zhi-yan,WANG Jian. Research on Feature Fusion Algorithm for Multimodal Emotion Recognition[J].,2016,26(05):27-30.
点击复制

 多模式情感识别特征参数融合算法研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年05期
页码:
27-30
栏目:
智能、算法、系统工程
出版日期:
2016-05-10

文章信息/Info

Title:
 Research on Feature Fusion Algorithm for Multimodal Emotion Recognition
文章编号:
1673-629X（2016)05-0027-04
作者:
 韩志艳; 王健
 渤海大学 工学院
Author(s):
 HAN Zhi-yan; WANG Jian
关键词:
 ; 多模式; 情感识别; 语音信号; 面部表情信号
Keywords:
 ; multimodal; emotion recognition; speech signal; facial expression signal
分类号:
TP391.4
文献标志码:
A
摘要:
 为了克服单模式情感识别存在的局限性,文中以语音信号和面部表情信号为研究对象,提出了一种新型的多模式情感识别算法.首先,将提取的语音信号和面部表情信号特征进行融合,然后通过有放回地抽样获得各训练样本集,并利用Adaboost算法训练获得各子分类器.再采用双误差异性选择策略来度量两两分类器之间的差异性.最后运用多数投票原则进行投票,得到最终识别结果.实验结果表明,该方法充分发挥了决策层融合与特征层融合的优点,使整个情感信息的融合过程更加接近人类情感识别,情感识别率达91.2%.
Abstract:
 In order to overcome the limitation of single mode emotion recognition,a novel multimodal emotion recognition algorithm is proposed,taking speech signal and facial expression signal as the research subjects. First,the speech signal feature and facial expression signal feature is fused,and sample sets by putting back sampling are obtained,and then sub-classifiers are acquired by Adaboost algo-rithm. Second,the difference is measured between two classifiers by double error difference selection strategy. Finally,the recognition re-sult is obtained by the majority voting rule. Experiments show the method improves the accuracy of emotion recognition by giving full play to the advantages of decision level fusion and feature level fusion,and makes the whole fusion process close to human emotion rec-ognition more,with a recognition rate 91. 2%.

相似文献/References:

[1]郭亮,田泽,刘娟,等.基于SOPC的多模式显示接口设计与实现[J].计算机技术与发展,2013,(07):191.
　GUO Liang,TIAN Ze,LIU Juan,et al.Design and Implementation of Multi-mode Image Display Interface Based on SOPC Technology[J].,2013,(05):191.
[2]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(05):1.
[3]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(05):5.
[4]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(05):13.
[5]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(05):21.
[6]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(05):25.
[7]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(05):29.
[8]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(05):34.
[9]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(05):38.
[10]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(05):43.

更新日期/Last Update: 2016-09-19