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Important Attributes Selection Based on Rough Set for Speech Emotion Recognition

Important Attributes Selection Based on Rough Set for Speech Emotion Recognition

Jian Zhou, Guoyin Wang, Yong Yang
Copyright: © 2011 |Pages: 10
ISBN13: 9781609605537|ISBN10: 1609605535|EISBN13: 9781609605544
DOI: 10.4018/978-1-60960-553-7.ch016
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MLA

Zhou, Jian, et al. "Important Attributes Selection Based on Rough Set for Speech Emotion Recognition." Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing, edited by Yingxu Wang, IGI Global, 2011, pp. 262-271. https://doi.org/10.4018/978-1-60960-553-7.ch016

APA

Zhou, J., Wang, G., & Yang, Y. (2011). Important Attributes Selection Based on Rough Set for Speech Emotion Recognition. In Y. Wang (Ed.), Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing (pp. 262-271). IGI Global. https://doi.org/10.4018/978-1-60960-553-7.ch016

Chicago

Zhou, Jian, Guoyin Wang, and Yong Yang. "Important Attributes Selection Based on Rough Set for Speech Emotion Recognition." In Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing, edited by Yingxu Wang, 262-271. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-553-7.ch016

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Abstract

Speech emotion recognition is becoming more and more important in such computer application fields as health care, children education, etc. In order to improve the prediction performance or providing faster and more cost-effective recognition system, an attribute selection is often carried out beforehand to select the important attributes from the input attribute sets. However, it is time-consuming for traditional feature selection method used in speech emotion recognition to determine an optimum or suboptimum feature subset. Rough set theory offers an alternative, formal and methodology that can be employed to reduce the dimensionality of data. The purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying important features in speech emotion recognition system. The experiments on CLDC emotion speech database clearly show this approach can reduce the calculation cost while retaining a suitable high recognition rate.

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