Makhraj recognition of Hijaiyah letter for children based on Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machines (SVM) method

Makhraj is the most important thing for Muslim to recite the Holy Quran properly besides of Tajweed. This paper describe the Makhraj recognition of Hijaiyah Letter for children education. To make the Makhraj recognition, the feature extraction is used Mel-Frequency Cepstrum Coefficients (MFCC) method and to classify the Hijaiyah letter use Support Vector Machines (SVM) method based on Python 2.7. The waveform analysis of each Hijaiyah Makhraj pronunciation shows the differences of each letter. The database of Hijaiyah Makhraj pronunciation using 12 feature extraction can be classified by SVM process.

[2]  Rajni Mehta Speech Recognition Techniques: A Review , 2014 .

[3]  Muhammad Azhar Zailaini,et al.  Analysis of tajweed errors in Quranic recitation , 2013 .

[4]  W. S. Mada Sanjaya,et al.  Sistem Kontrol Robot Arm 5 DOF Berbasis Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC) dan Adaptive Neuro-Fuzzy Inference System (ANFIS) , 2016 .

[5]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[6]  Nor Farizan Zakaria,et al.  Speech Processing for Makhraj Recognition The Design of Adaptive Filter for Noise Removal , 2011 .

[7]  Bassel Soudan,et al.  Computer-Aided Training for Quranic Recitation☆ , 2015 .

[8]  W. S. Mada Sanjaya,et al.  Speech Recognition using Linear Predictive Coding (LPC) and Adaptive Neuro-Fuzzy (ANFIS) to Control 5 DoF Arm Robot , 2018, Journal of Physics: Conference Series.

[9]  W S M Sanjaya,et al.  The Implementation of Speech Recognition using Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machine (SVM) method based on Python to Control Robot Arm , 2018 .

[10]  Zabidin Salleh,et al.  Implementasi Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients(MFCC) dan Adaptive Neuro-Fuzzy Inferense System(ANFIS) sebagai Kontrol Lampu Otomatis , 2014 .

[11]  N.W. Arshad,et al.  Speech processing for makhraj recognition , 2011, International Conference on Electrical, Control and Computer Engineering 2011 (InECCE).

[12]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[13]  Ranjan Parekh,et al.  Recognition of Isolated Words using Features based on LPC, MFCC, ZCR and STE, with Neural Network Classifiers , 2012 .

[14]  Thiang,et al.  SPEECH RECOGNITION USING LPC AND HMM APPLIED FOR CONTROLLING MOVEMENT OF MOBILE ROBOT , 2011 .

[15]  Zaidi Razak,et al.  Jawi Character Speech-to-Text Engine Using Linear Predictive and Neural Network for Effective Reading , 2009, 2009 Third Asia International Conference on Modelling & Simulation.

[16]  Kais Ouni,et al.  A novel phonemes classification method using fuzzy logic , 2013 .

[18]  Al-Sakib Khan Pathan,et al.  A Practical and Interactive Web-Based Software for Online Qur'Anic Arabic Learning , 2016, 2016 6th International Conference on Information and Communication Technology for The Muslim World (ICT4M).

[19]  R. Rizal Isnanto,et al.  Aplikasi Pengenalan Ucapan dengan Ekstraksi Mel-Frequency Cepstrum Coefficients (MFCC) Melalui Jaringan Syaraf Tiruan (JST) Learning Vector Quantization (LVQ) untuk Mengoperasikan Kursor Komputer , 2011 .

[20]  Xia Li,et al.  A robust keyword detection system for criminal scene analysis , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[21]  I Nyoman Kusuma Wardana,et al.  Identifikasi Biometrik Intonasi Suara untuk Sistem Keamanan Berbasis Mikrokomputer , 2014 .

[22]  Ines BEN FREDJ,et al.  Optimization of Features Parameters for HMM Phoneme Recognition of TIMIT Corpus , 2013 .

[23]  Ahmad Farid Abidin,et al.  Arabic letters corpus based Malay speaker-independent , 2013, 2013 IEEE 3rd International Conference on System Engineering and Technology.

[24]  Atul Kumar,et al.  Speech Recognition Based Wheelchair Using Device Switching , 2014 .

[25]  Suryo Wijoyo,et al.  Speech Recognition Using Linear Predictive Coding and Artificial Neural Network for Controlling Movement of Mobile Robot , 2011 .

[26]  Khalid Iqbal,et al.  Automatic Speech Recognition of Urdu Digits with Optimal Classification Approach , 2015 .

[27]  Muhammad Subali,et al.  Analysis of Fundamental Frequency and Formant Frequency for Speaker ‘Makhraj’ Pronunciation with DTW Method , 2016 .

[28]  Elvira Sukma Wahyuni,et al.  Arabic speech recognition using MFCC feature extraction and ANN classification , 2017, 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE).

[29]  Ratnadeep R. Deshmukh,et al.  KNN based emotion recognition system for isolated Marathi speech , 2015 .