Gender Classification in Speech Recognition using Fuzzy Logic and Neural Network

Kunjithapatham Meena1, Kulumani Subramaniam2, and Muthusamy Gomathy3
1Vice Chancellor, Bharathidhasan University, Principal and Director, India
2Department of Computer Application, Shrimathi Indira Gandhi College, India
3Department of Computer Science, Shrimathi Indira Gandhi College, India

 

Abstract: Nowadays classification of gender is one of the most important processes in speech processing. Usually gender classification is based on considering pitch as feature. The pitch value of female is higher than the male. In most of the recent research works gender classification process is performed using the abovementioned condition. In some cases the pitch value of male is higher and also pitch of some female is low, in that case this classification does not produce the exact required result. By considering the aforementioned problem we have here proposed a new method for gender classification method which considers three features. The new method uses fuzzy logic and neural network to identify the gender of the speaker. To train fuzzy logic and neural network, training dataset is generated by using the above three features. Then mean value is calculated for the obtained result from fuzzy logic and neural network. By using this threshold value, the proposed method identifies the speaker belongs to which gender. The implementation result shows the performance of the proposed technique in gender classification.

Keywords: Gender classification, fuzzy logic, neural network, energy entropy, short time energy, zero crossing rate.

 

Received July 16, 2011; accepted December 30, 2011

  

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