Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach

  • Duman Care Khrisne Udayana University
  • Theresia Hendrawati STMIK STIKOM Indonesia

Abstract

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.

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References

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Published
2020-02-29
How to Cite
KHRISNE, Duman Care; HENDRAWATI, Theresia. Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach. Journal of Electrical, Electronics and Informatics, [S.l.], v. 4, n. 1, p. 34-37, feb. 2020. ISSN 2622-0393. Available at: <https://ojs.unud.ac.id/index.php/JEEI/article/view/60184>. Date accessed: 13 july 2020. doi: https://doi.org/10.24843/JEEI.2020.v04.i01.p06.