Feature extraction analysis on Indonesian speech recognition system

Speech recognition is widely applied to speech to text, speech to emotion, in order to make gadget and computer easier to use, or to help people with hearing disability. Feature extraction is one of significant step in the performance of speech recognition. Therefore, the proper selection is really needed. In this paper, we analyze feature extraction that can have good performance for Indonesian speech recognition system. The feature extraction method that will be analyzed are Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficient (MFCC). Meanwhile, PNN is used as classification method in this study. The testing results show that MFCC is faster than LPC, but LPC can have the better accuracy. The accuracy of system is influenced by feature extraction, number of class and smoothing parameter.