Difficulties of Standard Arabic Phonemes Spoken by Non-Arab Primary School Children based on Formant Frequencies

Abdul-Kadir, Nurul Ashikin; Sudirman, Rubita

doi:10.3844/jcssp.2011.1003.1010

Cited by 12 publications

(14 citation statements)

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“…For future work, more than one of the represented features of Arabic speech such as: spectral envelope, onsets representation and formant frequencies can be tested separately and/or combined for accomplishing the process of segmentation [10]. Also, more constraints can be added to Peak picking to enhance the algorithm.…”

Section: Future Workmentioning

confidence: 99%

MFC peak based segmentation for continuous Arabic audio signal

Abdo

Kandil

Fawzy

2014

2nd Middle East Conference on Biomedical Engineering

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This paper presents an algorithm for segmenting a subset of emphatic and non-emphatic sounds automatically from continuously spoken Arabic speech. The important contribution of this paper is to generate rules for automatic segmentation of these sounds which can be extended to the rest of Arabic sounds. In addition, the findings can be used for other speech analysis problems such as data training for speech recognizers, continuous speech segmentation systems, or to build and label Arabic databases. This study has been done in context of recited principles of the Holy Quran which commonly known as recitation rules. The method developed is based on peaks detection from delta function of Mel Frequency Cepstral Coefficients MFCC .The peaks position is used for boundaries locating of the target sounds within the speech signal. Medium vocabulary speech database was used to evaluate the system performance. The test database contained 80 recited words. Each word was recorded from six different speakers constituting total of 480 words. A significant increase in accuracy rate has been achieved compared to the prior work of [1, 2] by enhancing the developed algorithm. Results show that the enhanced algorithm achieved a segmentation accuracy of up to 90%.

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Section: Future Workmentioning

confidence: 99%

MFC peak based segmentation for continuous Arabic audio signal

Abdo

Kandil

Fawzy

2014

2nd Middle East Conference on Biomedical Engineering

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“…From the spectrogram, the formants (F 1 , F 2 , F 3 and F 4 ) were studied and samples which fall in the average formants values were extracted. [19][20] These frequencies were obtained as in (3).…”

Section: Data Processingmentioning

confidence: 99%

Cascade–forward Neural Networks for Arabic Phonemes Based on k–Fold Cross Validation

et al. 2013

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The study of Malaysian Arabic phoneme is rarely found which make the references to the work is difficult. Specific guideline on Malaysian subject is not found even though a lot of acoustic and phonetics research has been done on other languages such as English, French and Chinese. In this paper, we monitored and analyzed the performance of cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. It is focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. Cascade neural networks are chosen as it provide less time for samples processing. The method, k-fold cross validation to evaluate each network architecture in k times to improve the reliability of the choice of the optimal architecture. Based on this method, namely 10-fold cross validation, the most suitable cascade-layer network architecture in first hidden layer and second hidden layer is 40 and 10 nodes respectively with MSE 0.0402. The training and testing recognition rates achieved were 94% and 93% respectively.

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“…These phonemes of letters have closely articulated phonemes and similar sound with the Malay language, but in fact, have different articulation points. Meanwhile, [27] have reported that, there are seven consonants (/q/-[‫,]ق‬ ‫,]ظ[-/∂/‬ /x/-[‫,]خ‬ /γ/-[‫,]غ‬ /H/-[‫,]ح‬ ‫]ع[-/?/‬ and /h/-[‫,)]ه‬ unable to give the promising results based on inappropriate values of formant frequencies measured using spectrogram. Thus, it is considered as difficult Arabic consonants to be articulated properly among Malaysian primary school students.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is considered as difficult Arabic consonants to be articulated properly among Malaysian primary school students. Experts and researchers had identified those phonemes after years of teaching the Malay students from all levels of educations [5], [8], [9], [27], and those misarticulate phonemes have been summarized in Table 1. Malaysian Journal of Computer Science.…”

Section: Introductionmentioning

confidence: 99%

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Robust Feature Extraction Based on Spectral and Prosodic Features for Classical Arabic Accents Recognition

Ibrahim

Idris

Rahman

et al. 2019

MJCS

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The variability of speech patterns produced by individuals is unique. The uniqueness is due to the accent influenced by the individual’s native dialect. Modeling individual variation of spoken language is a challenge under the Automatic Speech Recognition (ASR) field. The individual differences concerning of accent revealed the critical issues in Classical Arabic (CA) recitation among Malay speakers. This problem is caused by the misarticulate phonemes, which affected by the Malay colloquial dialect and native language. Most of ASR researchers are unable to understand the behavior of phonemes and speech patterns in CA, thus degrading the ASR performance. This paper focuses on identifying the accent of Malay speakers on the recitation of Sūrah Al-Fātiḥah with 7 Quranic accents, using the proposed feature extraction technique. In this work, the technique presented is a combination of spectral and prosodic features, which are mainly designed for accent in ASR. Differed with current conventional method, where the spectral feature alone has been applied for feature extraction in many ASR research. The prosodic elements in CA such as pitch, energy and spectral-tilt need to be taken into consideration, thus a significant variety of features for each phoneme able to help in distinguishing one accent from another. Meanwhile, the spectral representation of Mel-Frequency Cepstral Coefficients (MFCC) is utilized for the decorrelating property of the cepstrum. At present, Gaussian Mixture Models (GMM) has been applied for the classification stage. From experimental results, the system performance is the best when the prosodic is integrated with MFCC, alongside the GMM with 81.7%-89.6% of accuracy. It was 5.5%-7.3% increment as compared to MFCC alone.

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Difficulties of Standard Arabic Phonemes Spoken by Non-Arab Primary School Children based on Formant Frequencies

Abstract: Problem statement:

Cited by 12 publications

References 14 publications

MFC peak based segmentation for continuous Arabic audio signal

MFC peak based segmentation for continuous Arabic audio signal

Cascade–forward Neural Networks for Arabic Phonemes Based on k–Fold Cross Validation

Robust Feature Extraction Based on Spectral and Prosodic Features for Classical Arabic Accents Recognition

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