Pemodelan Harmonik untuk Pelafalan Makhraj Huruf Hijaiah

Muhammad Fadhlullah, Catur Atmaji
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Abstract

Learning to pronounce hijaiah letters needs to be assessed objectively, so it is necessary to form digital audio resulting from the synthesis of Harmonic Plus Residual (HPR) modeling, which conducted with two pronunciation methods, taskin and tasydid. The experiment consists data acquisition, signal cutting, framing and windowing, detection of fundamental and harmonic frequencies, synthesis of HPR, to produce synthetic signals. The results of the synthetic signals then analyzed qualitatively by signal spectrogram analysis and scoring.From the experimental results, it can be concluded that this study was ultimately unable to determine the best HPR parameters, but concluded that the tasydid method was the best method for learning pronunciation and for the HPR model synthesis. This is because the tasydid method with different parameters but all of them can produce good synthetic signal, both in terms of comparative analysis of similar signal spectrograms and from the results of scoring with an average value of 10. On the other hand, the taskin method harf shows unsatisfactory results, where the synthetic sound is mostly just noise, so the scoring results is under 9, and is reinforced by the results of the spectrogram comparison between the original and synthetic signals which visually different.
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Hijaiah拼写检查的谐波建模
学习发音hijaiah字母需要客观评估,因此有必要通过合成谐波加残差(HPR)模型来形成数字音频,该模型使用taskin和tasydid两种发音方法进行。实验包括数据采集、信号切割、成帧和开窗、基频和谐波频率的检测、HPR的合成,以产生合成信号。然后通过信号谱图分析和评分对合成信号的结果进行定性分析。从实验结果可以得出结论,本研究最终无法确定最佳的HPR参数,但得出结论,tasydid方法是学习发音和HPR模型合成的最佳方法。这是因为具有不同参数但所有参数的tasydid方法都可以产生良好的合成信号,无论是从相似信号频谱图的比较分析还是从平均值为10的评分结果来看。另一方面,taskin方法harf显示出不令人满意的结果,其中合成声音大多只是噪声,因此评分结果低于9,并且原始信号和合成信号之间的频谱图比较结果在视觉上有所不同,从而增强了评分结果。
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