Human Voice Recognition Using Artificial Neural Networks
Main Article Content
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..
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.