SISTEM PENGENALAN SUARA BAHASA INDONESIA UNTUK MENGENALI AKSEN DAERAH
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In performing theater sometimes feature regional stories that use the characteristic accents of the displayed areas and there is a theater that uses the linguist to know the accent from the area so that the characters who want to staged to deepen the role. However, sometimes theater does not have a linguist to know the accent used is correct or not. Therefore, it takes a technology that can recognize the Indonesian voice using accents that can help linguists recognize regional accents called speech recognition. Voice recognition process is divided into two main parts of the method of feature extraction and pattern recognition methods. In this research we use Linear Predictive Coding (LPC) characteristic extraction method and pattern recognition method using Vector Quantization (VQ). The results of this test, the built application can be used to recognize the sound of Indonesian language using accent areas of Malay and sunda. To recognize sounds more effective with sounds that use the sentence because the value of voice features that use more sentences than the value of voice traits that use the word. Thus, the results obtained with a high accuracy using the sentence that is 80% for Malay Accents sentence and 80% for the Sundanese accent sentence. Then, for the accuracy of using the word "pergi" is 40% for the word using accent Malay and 60% for the word using Sundanese accent. Meanwhile, for the accuracy of using the word "persib" which is 90% for the word using accents Malay and 70% for words using Sundanese accents. Keywords—Speech Recognition, Accents, Linear Predictive Coding, Vector Quantization
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A. Y. P. Idwal, Y. I. Nurhasanah, dan D. B. Utami, “SISTEM PENGENALAN SUARA BAHASA INDONESIA UNTUK MENGENALI AKSEN DAERAH”, JuTISI, vol. 3, no. 3, Des 2017.
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This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.