Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Feature selection in a kernel space
Proceedings of the 24th international conference on Machine learning
Feature relationships hypergraph for multimodal recognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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In speech based emotion recognition, both acoustic features extraction and features classification are usually time consuming,which obstruct the system to be real time. In this paper, we proposea novel feature selection (FSalgorithm to filter out the low efficiency features towards fast speech emotion recognition.Firstly, each acoustic feature's discriminative ability, time consumption and redundancy are calculated. Then, we map the original feature space into a nonlinear one to select nonlinear features,which can exploit the underlying relationship among the original features. Thirdly, high discriminative nonlinear feature with low time consumption is initially preserved. Finally, a further selection is followed to obtain low redundant features based on these preserved features. The final selected nonlinear features are used in features' extraction and features' classification in our approach, we call them qualified features. The experimental results demonstrate that recognition time consumption can be dramatically reduced in not only the extraction phase but also the classification phase. Moreover, a competitive of recognition accuracy has been observed in the speech emotion recognition.