The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature Selection with Selective Sampling
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
Emotive alert: HMM-based emotion detection in voicemail messages
Proceedings of the 10th international conference on Intelligent user interfaces
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
The Journal of Machine Learning Research
Audio-visual based emotion recognition-a new approach
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Analyze multiple emotional expressions in a sentence
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Segment-based emotion recognition from continuous Mandarin Chinese speech
Computers in Human Behavior
Relevance vector machine based speech emotion recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Dimensionality reduction-based spoken emotion recognition
Multimedia Tools and Applications
Skin cancer extraction with optimum fuzzy thresholding technique
Applied Intelligence
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Emotional expression and understanding are normal instincts of human beings, but automatical emotion recognition from speech without referring any language or linguistic information remains an unclosed problem. The limited size of existing emotional data samples, and the relative higher dimensionality have outstripped many dimensionality reduction and feature selection algorithms. This paper focuses on the data preprocessing techniques which aim to extract the most effective acoustic features to improve the performance of the emotion recognition. A novel algorithm is presented in this paper, which can be applied on a small sized data set with a high number of features. The presented algorithm integrates the advantages from a decision tree method and the random forest ensemble. Experiment results on a series of Chinese emotional speech data sets indicate that the presented algorithm can achieve improved results on emotional recognition, and outperform the commonly used Principle Component Analysis (PCA)/Multi-Dimensional Scaling (MDS) methods, and the more recently developed ISOMap dimensionality reduction method.