DEVELOPMENT OF AN OPTIMAL EXTRACTED FEATURE CLASSIFICATION SCHEME IN VOICE RECOGNITION SYSTEM USING DYNAMIC CUCKOO SEARCH ALGORITHM
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- NGN 3000
ABSTRACT
This research work is aimed at the development of an optimal extracted feature classification scheme in a voice recognition system using dynamic cuckoo search algorithm. This minimized error mismatch in the recognition process and increased accuracy of recognition. Standard voice dataset was obtained from English Language Speech Database for Speaker Recognition (ELSDSR) of the Technical University of Denmark (DTU), processed and key features of these voice data were extracted. A dynamic Cuckoo Search Algorithm (dCSA) was developed, which optimally classify the extracted feature vectors of the speech signals from the voice data for the voice recognition system (using the dataset obtained from ELSDSR database of the DTU. The performance of the developed Voice Recognition System (VRS) with dCSA-based scheme was compared with that of the standard CSA-based scheme using accuracy as performance metrics. The results of the dCSA-based classification scheme showed a recognition accuracy of 93.18% in the VRS when compared with that of the standard CSA-based classification scheme which records 90% accuracy. Simulation was carried out using MATLAB 2013b.
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