Abstract
Traditionally in speech emotion recognition, feature selection(FS) is implemented by considering the features from all classes jointly. In this paper, a hybrid system based on all-class FS and pairwise-class FS is proposed to improve speech emotion classification performance. Besides a subset of features obtained from an all-class structure, FS is performed on the available data from each pair of classes. All these subsets are used in their corresponding K-nearest-neighbors(KNN) or Support Vector Machine(SVM) classifiers and the posterior probabilities of the multi-classifiers are fused hierarchically. The experiment results demonstrate that compared with the classical method based on all-class FS and the pairwise method based on pairwise-class FS, the proposed approach achieves 3.2%-8.4% relative improvement on the average F1-measure in speaker-independent emotion recognition.
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Liu, J., Chen, C., Bu, J., You, M., Tao, J. (2007). Speech Emotion Recognition Based on a Fusion of All-Class and Pairwise-Class Feature Selection. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4487. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72584-8_22
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DOI: https://doi.org/10.1007/978-3-540-72584-8_22
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