Acoustic echo cancellation using adaptive filtering algorithms for Quranic accents (Qiraat) identification

Kamarudin, Noraziahtulhidayu; Al-Haddad, S. A. R.; Abushariah, Mohammad A. M.; Hashim, Shaiful Jahari; Hassan, Abd Rauf

doi:10.1007/s10772-015-9319-z

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Meanwhile, the performance gained from train-set able to achieve a better result of +5.477 enhancement. It is proved that, the proposed feature extraction technique able to compete and gave a better recognition results, compared to the previous research that implemented the spectral features only [12], [13], [15], [16], [18]. Since the integration of spectral and prosodic is performed and able to give a better result as compared to others, thus, the confusion matrix based-Quranic accents are highlighted in Table 8(a) and Table 8(b) below.…”

Section: Prosodic and Spectral Features Evaluationsmentioning

confidence: 82%

“…Industrial Revolution: Impact and Readiness Special Issue, 2019 The 39-dimensional features vector of MFCC using 13 MFCC with 13∆ and 13∆∆ features of acceleration coefficients, showed a better result compared to the 25-dimensional features vector, that using 13 MFCC with 12∆ without absolute Energy, and Cepstral Mean Normalization (CMN). The same 39-dimensional features vector of MFCC, also been conducted previously by [12], [13], [15], [16], [18], in their research. The only different is limited to the certain parameter related to the window size of 20ms and 25ms, implemented by [15], [18], respectively.…”

Section: Spectral Evaluationmentioning

confidence: 99%

“…It is probably because of the lack and unavailability of a speech database for Quranic accents, which leads to the major hurdle in conducting this kind of research. Research conducted previously by [11], [12], [13], [14] has used the recitation audio of the Quranic accents available on the web. Noises, echo, and reverberation effects might influence the downloaded audio samples from the web and thus disputed the overall performance results.…”

Section: Introductionmentioning

confidence: 99%

“…Spectral feature is referred to short-term cepstral feature that reflects the voice parameters and signal characteristics of the speakers in the frequency timedomain. The algorithm of spectral features, known as Linear Predictive Coding Coefficient (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC) have been carried out by [11], whereas MFCC only been implemented by [12], [13], [14] for the feature extraction phase. Meanwhile, the Quranic Arabic experiment without accent assessment has been conducted by [15], [16], using the local in-house database constructed by non-Arabs-male certified speakers, and audio in the web for Arabs-male certified speakers, respectively.…”

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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Section: Prosodic and Spectral Features Evaluationsmentioning

confidence: 82%

Section: Spectral Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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 authors in [4] propose an online speech recognition technique for verification of the Quranic verses. According to Kamarudin et al [5], the rules of the Quran verse are prone to additive noise. Therefore, they can affect the classification of Quranic results.…”

Section: Introductionmentioning

confidence: 99%

Quranic Reciter Recognition: A Machine Learning Approach

Khan¹,

Qamar²,

Hadwan³

2019

Adv. sci. technol. eng. syst. j.

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Recitation and listening of the Holy Quran with Tajweed is an essential activity as a Muslim and is a part of the faith. In this article, we use a machine learning approach for the Quran Reciter recognition. We use the database of Twelve Qari who recites the last Ten Surah of Quran. The twelve Qari thus represents the 12-class problem. Two approaches are used for audio representation, firstly, the audio is analyzed in the frequency domain, and secondly, the audio is treated as images through Spectrogram. The Mel Frequency Cepstral Coefficients (MFCC) and Pitch are used as the features for model learning in the first case. In the second case of audio as images, Auto-correlograms are used to extract features. In both cases, the features are learned with the classical machine learning which includes the Naïve Bayes, J48, and the Random Forest. These classifiers are selected due to their overall good performance in the state-of-the-art. It is observed that classifiers can efficiently learn the separation between classes, when the audio is represented by the MFCC, and the Pitch features. In such a case, we get 88% recognition accuracy with the Naïve Bayes and the Random Forest showing that Qari can be effectively recognized from the recitation of the Quranic verses.

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Modern Standard Arabic speech disorders corpus for digital speech processing applications

Alqudah,

Alshraideh,

Abushariah

et al. 2024

Int J Speech Technol

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Acoustic echo cancellation using adaptive filtering algorithms for Quranic accents (Qiraat) identification

Cited by 6 publications

References 20 publications

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

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

Quranic Reciter Recognition: A Machine Learning Approach

Modern Standard Arabic speech disorders corpus for digital speech processing applications

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