Signal-based feature extraction for makhraj emission point classification
Due to the similar sound of one letter to the others, mistakes might happen when pronouncing a hijaiyah letter. The reciter will not read the Quran correctly if they do not understand the relationship between the hijaiyah letter sound and its point of articulation. This study addresses the issue to...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institution of Engineering and Technology
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41947/1/Signal-based%20feature%20extraction%20for%20makhraj%20emission.pdf http://umpir.ump.edu.my/id/eprint/41947/2/Signal-based%20feature%20extraction%20for%20makhraj%20emission%20point%20classification_ABS.pdf http://umpir.ump.edu.my/id/eprint/41947/ https://doi.org/10.1049/icp.2022.2562 |
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| Summary: | Due to the similar sound of one letter to the others, mistakes might happen when pronouncing a hijaiyah letter. The reciter will not read the Quran correctly if they do not understand the relationship between the hijaiyah letter sound and its point of articulation. This study addresses the issue to recognize the nine points of articulation (throat, uvular, molar, palatal, alveolar, dental, alveolar dental, lip, and interdental) from makhraj recitation using speech processing technique. As much as 181 non-distributive audio samples recorded in control environment. The input speech is a sukun combination of the Hijaiyah letter from an expert reciter. The research uses 5 type of signal-based feature extraction methods (MFCC, chroma, Mel spectrogram, spectral contract, and Tonnetz) and three type of classification methods (ANN, kNN, and SVM). The result shows the proposed method obtained a fair accuracy with the highest accuracy is 56% using ANN. |
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