Metode Linear Predictive Coding (LPC) Pada klasifikasi Hidden Markov Model (HMM) Untuk Kata Arabic pada penutur Indonesia

Abstract

Abstract— Arabic language has a slightly different pronunciation than the Indonesian so to learn it takes a long time. In Arabia itself, there are variants in the pronunciation of the Arabic language or dialect. Dialect is a language, and letters are used by a particular group of people in a clump that makes the difference between the readings even greeting one another. In Indonesia, alone speakers of Indonesia itself have a different dialect to native speakers.

This study was analyzed of Arabic writing suitability by Indonesian speakers using  Linear Predictive Coding extraction techniques. The text produces different patterns of speech. This also happens if the text is spoken by a speaker who is not the mother tongue of the speakers. The data training in this study is using the Arabic speaker sound. The feature extraction is classified using Hidden Markov Model.

In the classification, using Hidden Markov Model, voice signal is analyzed and searched the maximum possible value that can be recognized. The modeling results obtained parameters are used to compare with the sound of Arabic speakers. From the test results' Classification, Hidden Markov Models with Linear Predictive Coding extraction average accuracy of 78.6% for test data sampling frequency of 8,000 Hz, 80.2% for test data sampling frequency of 22050 Hz, 79% for frequencies sampling test data at 44100 Hz.