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

Marlina, Lina; Wardoyo, Cipto; Sanjaya, W. S. Mada; Anggraeni, Dyah; Dewi, Sinta Fatmala; Roziqin, Akhmad; Maryanti, Sri

doi:10.1109/icoiact.2018.8350684

Cited by 16 publications

(12 citation statements)

References 12 publications

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“…This is a filtering process aiming to obtain a smoother spectral form of the speech signals frequency and to reduce noise during speech retrieval [19]. The mathematical equation of the preemphasis can be seen in Eq.…”

Section: Preemphasismentioning

confidence: 99%

A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

Hidayat¹,

Winursito²

2021

IJIES

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Research on the current speech recognition system leads to the creation of a noise-resistant system. The Mel Frequency Cepstral Coefficients (MFCC) extraction method becomes a popular method in the speech recognition system. In this paper, the MFCC's weakness of noise interference is the main reason underlies the accomplishment of a robust speech recognition system. Development was carried out by improving the denoising performance using a wavelet transform. Modifications were carried out by analyzing the weakness of the wavelet denoising process on the recognition system using the MFCC method. The analysis was conducted at one of the MFCC stages, the Fast Fourier Transform (FFT) stage. The proposed method was conducted by performing the denoising process using Wavelet only on the noise-related data based on the FFT process' analysis results. The study utilized speech data in the form of eleven isolated words in English added with noise with several different characteristics. Results showed that the proposed method was capable of generating a better accuracy than conventional wavelet denoising methods on the signal to noise ratio (SNR) of 10dB, 15dB, and 20dB using a Fejer Korovkin 6 wavelet type. The highest accuracy increase of the proposed method was in signal to noise ratio (SNR) of 15dB with a rise of 4.63%, followed by a 3.96% increase at 20dB intensity, and 2.3% at 10dB intensity. The performance of the proposed method is then compared with other methods. The results show that the proposed method has the best performance on clean speech and noisy speech at SNR intensities of 10dB, 15dB, and 20dB.

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Section: Preemphasismentioning

confidence: 99%

A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

Hidayat¹,

Winursito²

2021

IJIES

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“…Speech recognition commonly used in Arabic and Quran recitation are such as Arabic coding and synthesis research, dialect detection, speaker recognition, memorization and sentence retrieval. A large number of analyses use word or sentence utterance approach techniques [2] to identify and evaluate from signal speech representation. Spoken or readings are present as a form of language because speech also contains basic acoustic sounds or also known as phonemes.…”

Section: Momentous Fragmentary Mfcc-formantsmentioning

confidence: 99%

“…BIC is an asymptotically optimal method for estimating the best model using only sample estimates [23]. BIC is defined as (2) where x are the sample data, l(x,M) is the maximized likelihood function under a model M. While k is the number of estimated parameters, and n is the sample size.…”

Section: B Feature Extraction and Prediction Modelmentioning

confidence: 99%

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Adam¹,

Shafie²,

Abas³

et al. 2021

IJACSA

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The use of technological speech recognition systems with a variety of approaches and techniques has grown rapidly in a variety of human-machine interaction applications. Further to this, a computerized assessment system to identify errors in reading the Qur'an can be developed to practice the advantages of technology that exist today. Based on Quranic syllable utterances, which contain Tajweed rules that generally consist of Makhraj (articulation process), Sifaat (letter features or pronunciation) and Harakat (pronunciation extension), this paper attempts to present the technological capabilities in realizing Quranic recitation assessment. The transformation of the digital signal of the Quranic voice with the identification of reading errors (based on the Law of Tajweed) is the main focus of this paper. This involves many stages in the process related to the representation of Quranic syllable-based Recitation Speech Signal (QRSS), feature extraction, non-phonetic transcription Quranic Recitation Acoustic Model (QRAM), and threshold classification processes. MFCC-Formants are used in a miniature state that are hybridized with three bands in representing QRSS combined vowels and consonants. A human-guided threshold classification approach is used to assess recitation based on Quranic syllables and threshold classification performance for the low, medium, and high band groups with performances of 87.27%, 86.86%and 86.33%, respectively.

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“…The resulted recognition rate is 91.95% (ayates) and 86.41% (phonemes). One more purely educational solution is proposed by Marlina et al (2018). This study is more focused on a specific and basic rule of tajweed which is called Makhraj.…”

Section: Related Workmentioning

confidence: 99%

Qee: Vr Game That Aims to Test and Enhance the Players Knowledge of Tajweed

AlKhateeb¹,

AlSamel²,

AlBarazi³

et al. 2019

Proceedings of the International Conferences Interfaces and Human Computer Interaction 2019 Game and Entertainment Technologies

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This paper aims to provide an effective computer game which, in addition to entertaining players, helps improving their knowledge of Tajweed. Tajweed is an Islamic science that studies the correct recitation of the Qur'an words and letters. It has been proven that learning at an early age gives more reliable and long-lasting knowledge. In addition, teenagers are more capable of controlling their pronunciation and modifying them. For these reasons, Qee game targets the ages between 13 and 18. As teenagers nowadays are exposed to a variety of game technologies, which raised their expectations and satisfaction level, a 3D VR game was introduced as an attractive modern solution. A prototype of two levels was developed and tested by 20 participants. The testing session resulted in a successful outcome with some suggestions for future modifications.

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

Cited by 16 publications

References 12 publications

A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Qee: Vr Game That Aims to Test and Enhance the Players Knowledge of Tajweed

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

Cited by 16 publications

References 12 publications

A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Qee: Vr Game That Aims to Test and Enhance the Players Knowledge of Tajweed

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Product

Resources

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Qee: Vr Game That Aims to Test and Enhance the Players Knowledge of Tajweed