东南大学学报（英文版）2016，Vol.32Issue(2)：151-157,7.DOI:10.3969/j.issn.1003-7985.2016.02.004
基于级联降维判别的语言情感识别
Speech emotion recognition via discriminant-cascading dimensionality reduction
摘要
Abstract
In order to accurately identify speech emotion information, the discriminant-cascading effect in dimensionality reduction of speech emotion recognition is investigated. Based on the existing locality preserving projections and graph embedding framework, a novel discriminant-cascading dimensionality reduction method is proposed, which is named discriminant-cascading locality preserving projections ( DCLPP ) . The proposed method specifically utilizes supervised embedding graphs and it keeps the original space for the inner products of samples to maintain enough information for speech emotion recognition. Then, the kernel DCLPP ( KDCLPP ) is also proposed to extend the mapping form. Validated by the experiments on the corpus of EMO-DB and eNTERFACE’05, the proposed method can clearly outperform the existing common dimensionality reduction methods, such as principal component analysis ( PCA ) , linear discriminant analysis ( LDA ) , locality preserving projections ( LPP ) , local discriminant embedding ( LDE) , graph-based Fisher analysis ( GbFA) and so on, with different categories of classifiers.关键词
语音情感识别/级联降维的保局投影算法/判别分析/降维Key words
speech emotion recognition/discriminant-cascading locality preserving projections/discriminant analysis/dimensionality reduction分类
信息技术与安全科学引用本文复制引用
王如刚,徐新洲,黄程韦,吴尘,张昕然,赵力..基于级联降维判别的语言情感识别[J].东南大学学报（英文版）,2016,32(2):151-157,7.基金项目
The National Natural Science Foundation of China ( No.61231002,61273266), the Ph. D. Program Foundation of Minis-try of Education of China ( No.20110092130004), China Postdoctoral Science Foundation ( No.2015M571637) （ No.61231002,61273266）
