Open AccessClassification of Emotional Speech of Children Using Probabilistic Neural Network
Author(s) -
Hemanta Kumar Palo,
Mihir Narayan Mohanty
Publication year - 2015
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v5i2.pp311-317
Subject(s) - computer science , classifier (uml) , speech recognition , artificial intelligence , probabilistic neural network , probabilistic classification , probabilistic logic , hindi , pattern recognition (psychology) , artificial neural network , emotion classification , machine learning , natural language processing , support vector machine , time delay neural network , naive bayes classifier
Child emotions are highly flexible and overlapping. The recognition is a difficult task when single emotion conveys multiple informations. We analyze the relevance and importance of these features and use that information to design classifier architecture. Designing of a system for recognition of children emotions with reasonable accuracy is still a challenge specifically with reduced feature set. In this paper, Probabilistic neural network (PNN) has been designed for such task of classification. PNN has faster training ability with continuous class probability density functions. It provides better classification even with reduced feature set. LP_VQC and pH vectors are used as the features for the classifier. It has been attempted to design the PNN classifier with these features. Various emotions like angry, bore, sad and happy have been considered for this piece of work. All these emotions have been collected from children in three different languages as English, Hindi, and Odia. Result shows remarkable classification accuracy for these classes of emotions. It has been verified in standard databse EMO-DB to validate the result.