Deep Learning Long-Short Term Memory (LSTM) for Indonesian Speech Digit Recognition using LPC and MFCC Feature

Abstract

This paper presents Indonesian speech digit of decimal number (0–9) recognition using Deep Learning Long-Short Term Memory (LSTM). The LPC (Linear Predictive Coding) and MFCC (Mel-Frequency Cepstrum) feature extraction was used as an input on the LSTM model and the level of recognition accuracy was compared. The LPC feature extract speech feature based on a pitch or fundamental frequency, while MFCC extract speech feature based on the sound spectrum. We used 7990 speech digits consisted of 12 LPC coefficients and 12 MFCC coefficients as training data, while 790 data was used to classify on LSTM that had been trained. The results show that using LSTM for recognize Indonesian speech digit, the MFCC feature extraction gets better accuracy result of 96.58% compared to the LPC feature extraction which amounts to 93.79 %.