APLIKASI PENCIRIAN DENGAN LINEAR PREDICTIVE CODING UNTUK PEMBELAJARAN PENGUCAPAN NAMA HEWAN DALAM BAHASA INGGRIS MENGGUNAKAN JARINGAN SARAF TIRUAN PROPAGASI BALIK

Abstract

Abstract In this research designed a recognition system for learning the pronunciation of the word animal names in English . Original speech signal sample at 8000 Hz pick out a small portion For voice parameter extraction process used method Linear Predictive Coding ( LPC​​) to obtain cepstral coefficients . LPC cepstral coefficients are transformed into the frequency domain with Fast Fourier Transform ( FFT). For decision making process of the introduction and use Neural Networks ( NN) back propagation . Testing is done using the data train , according to a database of test data and test data do not fit database. While the networks do a variation of 3, 4 and 5 hidden layers respectively for 1 , 2 and 3 the number of syllables said . Based on the results of testing training data , the recognition rate for each variation of each network the number of syllables showed no difference in test results, the percentage was 99 % for the 1 syllable , 98.5 % for the 2 syllables and 100 % for 3 syllables. Test data suitable for testing the database , the highest recognition rate for type 1 syllable is a network with 4 hidden layers using a variation of the percentage is 85 %, whereas type 2 syllables highest recognition rate using a variation of 5 hidden layers with the correct percentage of 75 % and 81.67 % for type 3 syllables using 5 hidden layers . While the test results do not fit the test database, the highest recognition rate for type 1 syllable is a network with 4 hidden layers using a variation of the percentage is 15.83 % while the type 2 syllables highest recognition rate using a variation of 3 hidden layer s with percentage correct , 20. 83 % and 33.33 % for type 3 syllables using 3 and 4 hidden layers . Keywords : Linear Predictive Coding, Fast Fourier Transform, Neural Network, Backpropagation.