.png){kind=icon}
返回
Dimensionality reduction-based spoken emotion recognition
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-011-0887-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To improve effectively the performance on spoken emotion recognition, it is needed to perform nonlinear dimensionality reduction for speech data lying on a nonlinear manifold embedded in a high-dimensional acoustic space. In this paper, a new supervised manifold learning algorithm for nonlinear dimensionality reduction, called modified supervised locally linear embedding algorithm (MSLLE) is proposed for spoken emotion recognition. MSLLE aims at enlarging the interclass distance while shrinking the intraclass distance in an effort to promote the discriminating power and generalization ability of low-dimensional embedded data representations. To compare the performance of MSLLE, not only three unsupervised dimensionality reduction methods, i.e., principal component analysis (PCA), locally linear embedding (LLE) and isometric mapping (Isomap), but also five supervised dimensionality reduction methods, i.e., linear discriminant analysis (LDA), supervised locally linear embedding (SLLE), local Fisher discriminant analysis (LFDA), neighborhood component analysis (NCA) and maximally collapsing metric learning (MCML), are used to perform dimensionality reduction on spoken emotion recognition tasks. Experimental results on two emotional speech databases, i.e. the spontaneous Chinese database and the acted Berlin database, confirm the validity and promising performance of the proposed method.
Keyword:
Emotion recognition
Dimensionality reduction
Manifold learning
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏分享收藏Multi-stage classification of emotional speech motivated by a dimensional emotion model基于维度情感模型的情感语音多阶段分类
PREAI
分享收藏
查看更多内容