Human Voice Recognition Using Artificial Neural Networks

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Heri Nurdiyanto, et. al.

Abstract

Sound is one of the unique and distinguishable parts of the human body. Voice recognition technology is one of biometric technology that does not require much cost and does not require specialized equipment. One of the techniques of speech recognition is with artificial neural networks, where this method uses a working principle similar to the workings of the human brain. This thesis aims to apply artificial neural networks to voice recognition and create programs that simulate this method using Matlab 7.1 software.


The data used in the form of sound recordings are converted into numerical values with the Linear Predictive Coding process. The steps taken in Linear Predictive Coding include Pre-emphasis process, frame blocking, windowing Autocorrelation Analysis, Linear Predictive Coding Analysis, and change the Linear Predictive Coding parameter to the cepstral coefficient. This cepstral coefficient is a series of observations used as inputs on artificial neural networks, and will also be used for the training and testing process. In this research, artificial neural network architecture used is Learning Vector Quantization. In the process of Learning Vector Quantization neural network training using data as many as 35 votes, with learning rate 0.01, max depth 100, dec alpha 0.02 and min alpha 0,00001. Validation test results for 15 votes, obtained the conclusion that 73.34% of all validation votes successfully recognized..

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