Text Dependent Speakers Pattern Classification with Back Propagation Neural Network
N K Kaphungkui1, Gurumayum Robert Michael2, Aditya Bihar Kandali3
1N K Kaphungkui, Department Of Electronics and Communication, Dibrugarh University, Assam. India.
2Gurumayum Robert Michael, Department Of Electronics and Communication, Dibrugarh University, Assam, India.
3Dr Aditya Bihar Kandali, Electrical Department, Jorhat Engineering College, Assam, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9139-9143 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8889118419/2019©BEIESP | DOI: 10.35940/ijrte.D8889.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Speaker Recognition is the procedure of validating a speaker’s claimed identity using his/her speech characteristics which is unique to each individual. The primary objective of all speech recognition system is a man-machine interface which grants access into the system with the voice characteristics. This will served as a highly secure biometric system where security is the primary concern. The primary aim of this paper is to classify each speaker accurately with MFCC and Back Propagation Neural Network. Scaled conjugate gradient training function is used for back propagation neural network. A small database of 10 people is created from a group of five male and five female uttering the same sentence five times repeatedly. The sentence consists of five different words. The numbers of data set for classification is 22182.The accuracy obtained from the classification is 92.1% with small percentage of 7.9% misclassification which is acceptable good. The tool for simulation is MATLAB.
Keywords: Speaker Recognition, MFCC, Text Dependent, Confusion Matrix, Training, Validation, Testing..
Scope of the Article: Classification.