DEVELOPMENT OF AN OPTIMAL EXTRACTED FEATURE CLASSIFICATION SCHEME IN VOICE RECOGNITION SYSTEM USING DYNAMIC CUCKOO SEARCH ALGORITHM
DEVELOPMENT OF AN OPTIMAL EXTRACTED FEATURE CLASSIFICATION SCHEME IN VOICE RECOGNITION SYSTEM USING DYNAMIC CUCKOO SEARCH ALGORITHM
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Date
2017-12
Authors
YUSUF, Sanusi Audee
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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.
Description
A DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF SCIENCE (M.Sc) DEGREE IN CONTROL ENGINEERING DEPARTMENT OF COMPUTER ENGINEERING, FACULTY OF ENGINEERING, AHMADU BELLO UNIVERSITY, ZARIA, NIGERIA
Keywords
DEVELOPMENT,, OPTIMAL,, EXTRACTED,, CLASSIFICATION SCHEME,, VOICE RECOGNITION SYSTEM,, DYNAMIC CUCKOO SEARCH,, ALGORITHM,