Nigerian Language Simulated Speaker Verification System Using Back-Propagation Neural Network
Journal Title: IOSR Journals (IOSR Journal of Electrical and Electronics Engineering) - Year 2018, Vol 13, Issue 5
Abstract
This paper presents the use of Back propagation neural network to implement speaker verification simulated with Nigeria Languages. The focus is to verify voice patterns for different people speaking different Nigeria languagesso as to recognize (verify) their speech electronically.The voice samples of the people utilized were captured and then processed using the sound forge 9.0 software.The frequencies of each voice signals were used to train a backpropagation neural network, which inturn verifies the speakerthrough the voice patterns. Six neural networks (K1-1, K2-1, Y1-1, Y2-1, F1-1, and F2-1)were developed for training, testing, and validation ofthree selected Nigeria languages words from nine (9) people (three for each language). Discrete Fourier Transform (DFT) was usedto extract features from the voice samples for the backpropagation neural network (BPNN) training, testing, and validation. The network’s performance analysis results as deduced from regression analysis showsan averageoverall R-value of 0.9371 for acceptance, and an average of 0.3414 for rejection. The results obtained shows that each network verified the speaker it was trained for adequately. The results obtained from this work can be generalized to cater for larger vocabularies and for continuous speaker verification processes.
Authors and Affiliations
Shakiru Olajide KASSIM, Mohammed ABDUL- FATAU
Keywords
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- EP ID EP402411
- DOI 10.9790/1676-1305016270.
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