Language Independent Recognition of Human Emotion using Artificial Neural Networks

• Published in: International journal of cognitive informatics & natural intelligence Vol. 2; no. 3; pp. 1 - 21
• Main Authors: Bhatti, Muhammad Waqas, Wang, Yongjin, Guan, Ling
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.07.2008
• Subjects: Artificial neural networks; Emotion recognition; Emotions; General regression neural networks; Microcomputers; Neural networks; United States
• ISSN: 1557-3958; 1557-3966
• DOI: 10.4018/jcini.2008070101

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.