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.
Keywords: Mel Frequency Cepstral Coefficients, linier predictive coding, speech recognition
Author: Sukmawati Nur Endah
Journal Code: jptkomputergg170105

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