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Language Independent Recognition of Human Emotion using Artificial Neural Networks

Language Independent Recognition of Human Emotion using Artificial Neural Networks

Muhammad Waqas Bhatti, Yongjin Wang, Ling Guan
Copyright: © 2008 |Volume: 2 |Issue: 3 |Pages: 21
ISSN: 1557-3958|EISSN: 1557-3966|ISSN: 1557-3958|EISBN13: 9781615201938|EISSN: 1557-3966|DOI: 10.4018/jcini.2008070101
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MLA

Bhatti, Muhammad Waqas, et al. "Language Independent Recognition of Human Emotion using Artificial Neural Networks." IJCINI vol.2, no.3 2008: pp.1-21. http://doi.org/10.4018/jcini.2008070101

APA

Bhatti, M. W., Wang, Y., & Guan, L. (2008). Language Independent Recognition of Human Emotion using Artificial Neural Networks. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2(3), 1-21. http://doi.org/10.4018/jcini.2008070101

Chicago

Bhatti, Muhammad Waqas, Yongjin Wang, and Ling Guan. "Language Independent Recognition of Human Emotion using Artificial Neural Networks," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 2, no.3: 1-21. http://doi.org/10.4018/jcini.2008070101

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Abstract

This article presents a language-independent emotion recognition system for the identification of human affective state in the speech signal. A group of potential features are first identified and extracted to represent the characteristics of different emotions. To reduce the dimensionality of the feature space, whilst increasing the discriminatory power of the features, we introduce a systematic feature selection approach which involves the application of sequential forward selection (SFS) with a general regression neural network (GRNN) in conjunction with a consistency-based selection method. The selected parameters are employed as an input to the modular neural network, consisting of sub-networks, where each sub-network specializes in a particular emotion class. Comparing with the standard neural network, this modular architecture allows decomposition of a complex classification problem into small subtasks such that the network may be tuned based on the characteristics of individual emotion. The performance of the proposed system is evaluated for various subjects, speaking different languages.

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