Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm

Y. A. Gerhana, Aaz Muhammad Hafidz Azis, D. R. Ramdania, Wildan Budiawan Dzulfikar, A. R. Atmadja, D. Suparman, Ayu Puji Rahayu
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引用次数: 0

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
基于卷积神经网络算法的Hijaiyah字母发音自动检测
摘要:语音识别技术被用于学习古兰经中的字母。本研究旨在实现CNN算法在引入hijaiyah字母读音的结果识别中。使用Mel-frequency倒谱系数(MFCC)模型提取语音,然后使用CNN算法的深度学习模型进行分类。本系统采用CRISP-DM模型开发。通过对28个hijaiyah字母的616个语音数据进行测试,得到了准确率为62.45%、准确率为75%、召回率为50%、f1得分为58%的最佳值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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