计算机工程2017，Vol.43Issue(8)：306-309,315,5.DOI:10.3969/j.issn.1000-3428.2017.08.052
基于聚合经验模态分解的情感语音特征提取
Feature Extraction of Emotional Speech Based on Ensemble Empirical Mode Decomposition
摘要
Abstract
Extracting features of emotional speech signal is particularly important in the emotional speech recognition systems,which determines the overall recognition performance.The traditional feature extraction techniques assume speech signal is linear and short-stationary,without self-adapability.By using the Ensemble Empirical Mode Decomposition(EEMD)algorithm,the features are extracted in a nonlinear way.First,the emotional speech signal is decomposed into a series of Intrinsic Mode Function(IMF)by EEMD and effective IMFs set is selected using correlation coefficient method.Then the IMF Energy(IMFE)characteristics are obtained through calculation of the function in the set.In the experiment,Berlin speech database is chosen as the data source.IMFE features,prosodic features,Mel-Fregurecy Cepstrum Coefficients(MFCC)features and the fusion features of the three are input inte SVM respectively.The recognition results of different feature combinations are compared to validate the performance of the IMFE features.The experimental results show that the average recognition rate of IMFE feature merging with acoustic feature can reach 91.67％,and IMFE can effectively distingwish between different states.关键词
特征提取/聚合经验模态分解/固有模态函数/SpearmanRank相关系数/声学特征/情感语音识别Key words
feature extraction/Ensemble Empirical Mode Decomposition(EEMD)/Intrinsic Mode Function(IMF)/Spearman Rank correlation coefficient/acoustic feature/emotional speech recognition分类
信息技术与安全科学引用本文复制引用
张乐,张雪英,孙颖,张卫..基于聚合经验模态分解的情感语音特征提取[J].计算机工程,2017,43(8):306-309,315,5.基金项目
国家自然科学基金(61371193) （61371193）
山西省回国留学人员科研基金(2013-034). （2013-034）
