Fuzzy logic and neural network based gender classification using three features

Abstract

Gender classification is one of the most important processes in speech processing. Generally gender classification is done by considering pitch as feature. Normally the pitch value of female is higher than the male. By using this condition, gender classification process takes place. But in some case the pitch value of male is higher and also pitch of female is low, in that case this classification does not provide the exact result. By considering the abovementioned drawback, here proposed a new method for gender classification in speech processing which considers three features and uses fuzzy logic and neural network to identify the gender of the speaker. The features considered in the proposed method is energy entropy, short time energy and zero crossing rate. For training fuzzy logic and neural network, training dataset is generated using the above three features. Then mean value is computed from the result obtained from fuzzy logic and neural network. The gender classification is done by using this mean value. The implementation result shows the performance of the proposed technique in gender classification.