Comparison of Feature Extraction MFCC and LPC in Automatic Speech Recognition for Indonesian
Abstract: Speech recognition
can be defined as the process of converting voice signals into the ranks of the
word, by applying a specific algorithm that is implemented in a computer
program. The research of speech recognition in Indonesia is relatively limited.
This paper has studied methods of feature extraction which is the best among
the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients
(MFCC) for speech recognition in Indonesian language. This is important because
the method can produce a high accuracy for a particular language does not
necessarily produce the same accuracy for other languages, considering every
language has different characteristics. Thus this research hopefully can help
further accelerate the use of automatic speech recognition for Indonesian
language. There are two main processes in speech recognition, feature
extraction and recognition. The method used for comparison feature extraction
in this study is the LPC and MFCC, while the method of recognition using Hidden
Markov Model (HMM). The test results showed that the MFCC method is better than
LPC in Indonesian language speech recognition.
Author: Sukmawati Nur Endah
Journal Code: jptkomputergg170105
