Open AccessIndonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach
Author(s) -
Duman Care Khrisne,
Theresia Hendrawati
Publication year - 2020
Publication title -
journal of electrical, electronics and informatics
Language(s) - English
Resource type - Journals
eISSN - 2622-0393
pISSN - 2549-8304
DOI - 10.24843/jeei.2020.v04.i01.p06
Subject(s) - computer science , speech recognition , convolutional neural network , feature (linguistics) , artificial neural network , artificial intelligence , indonesian , mel frequency cepstrum , pattern recognition (psychology) , literacy , feature extraction , linguistics , philosophy , economics , economic growth
Games are considered capable of being used as a learning medium that can help teachers to teach children how to pronounce the Indonesian alphabet in early literacy, we try to build one aspect of the game in this study. The approach we use is a speech recognition approach that uses the convolutional neural network method. The results of this study indicate that CNN can recognize speech, with input data is in the form of sound. We use the MFCC feature vector sound feature to make a 3-dimensional matrix of input sound into CNN input. We also use the Sequential CNN architecture made from a simple 10 layer neural network, which produces a model with a small size, approximately only about 6 MB, with high accuracy (84%) and an F-Measure of 0.91.