中文摘要：

为了准确地识别语音情感信息,研究了语音情感识别的降维中判别级联效应.基于现有的局部投影算法和图形嵌入理论,提出了一种新型判别分析算法,即DCLPP算法.为了能够对语音情感识别保持足够的信息,该算法利用嵌入图形为样本的内部特点保留了原始空间.然后,为了扩展映射形式,提出了一种kernel dCLPP（KDCLPP）的方法.在EM O-DB和eNTERFACE＇05情感语音数据库上对该算法进行了验证,结果表明,所提算法可明显地超越现有的常用主成成分分析（PCA）、线性判别分析（LDA）、局部保持投影（LPP）、局部鉴别嵌入（LDE）和图优化的Fisher判别分析（Gb FA）等判别分析算法,这些算法都有不同类型的分类器.

英文摘要：

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.