Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Affective computing
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
Modeling drivers' speech under stress
Speech Communication - Special issue on speech and emotion
The role of voice quality in communicating emotion, mood and attitude
Speech Communication - Special issue on speech and emotion
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Toward computers that recognize and respond to user emotion
IBM Systems Journal
A facial expression recognition system based on supervised locally linear embedding
Pattern Recognition Letters
Locality preserving projections
Locality preserving projections
Emotional Speech Analysis on Nonlinear Manifold
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Ensemble methods for spoken emotion recognition in call-centres
Speech Communication
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Emotion Recognition in Chinese Natural Speech by Combining Prosody and Voice Quality Features
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Supervised locally linear embedding with probability-based distance for classification
Computers & Mathematics with Applications
Robots with emotional intelligence
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Acoustic feature selection for automatic emotion recognition from speech
Information Processing and Management: an International Journal
Automatic Choice of the Number of Nearest Neighbors in Locally Linear Embedding
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Manifold based analysis of facial expression
Image and Vision Computing
Multi-stage classification of emotional speech motivated by a dimensional emotion model
Multimedia Tools and Applications
Spoken emotion recognition through optimum-path forest classification using glottal features
Computer Speech and Language
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Regularization parameter choice in locally linear embedding
Neurocomputing
Computer Speech and Language
Detecting emotional state of a child in a conversational computer game
Computer Speech and Language
Combining acoustic features for improved emotion recognition in mandarin speech
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
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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.