Approach for Recognizing Allophonic Sounds of the Classical Arabic Based on Quran Recitations

Elhadj, Yahya O. Mohamed; Alghamdi, Mohammed H.; Alkanhal, Mohamed

doi:10.1007/978-3-642-45008-2_5

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…Comparing the extent of work in the domain of Classical Arabic, we found that MSA remains the focus of speech scientists mostly. Recently some contribution has been made in making computers, learn to recognize Holy Quran at phoneme and allophone levels [9,47]. As all reciters followed 'Tajwid' rules in recitation, it implies to keep same phoneme-level transcription for all reciters.…”

Section: Data Preparationmentioning

confidence: 99%

Discriminative Training for Phonetic Recognition of the Holy Quran

2015

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This paper presents the development of Holy Quran recitation recognizer. The decoder of recognizer performs sub-word level recognition at phoneme. The paper demonstrates high recognition accuracies achieved by applying incremental refinements to the HMM models of the phonemes during the training stage. The Maximumlikelihood (ML) criterion is first applied for HMMs parameter estimation, which produces average recognition accuracies of up to 83 %. This is followed by discriminative technique of minimum phone error (MPE), which is applied to minimize recognition error at phoneme level. Investigation shows that MPE based acoustic models improve generalization. The results show 3-4 % improvement in recognition accuracies, which are promising when compared with the case of ML approach applied alone.

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Section: Data Preparationmentioning

confidence: 99%

Discriminative Training for Phonetic Recognition of the Holy Quran

2015

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“…Based on Quranic recitations, researchers in [30] reported the construction of a recognizer for the allophonic sounds of Classical Arabic. The Cambridge HTK tools were used to create this recognizer.…”

Section: Nahar Et Al Inmentioning

confidence: 99%

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

Hadwan¹,

Alsayadi²,

Al-Hagree³

2023

Computers, Materials &Amp; Continua

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The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-toend transformer-based models for the Arabic language, as the researchers' community pays little attention to it. The Muslims Holy Qur'an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur'an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic model. A Mel filter bank is used for feature extraction. To build a language model (LM), the Recurrent Neural Network (RNN) and Long short-term memory (LSTM) were used to train an n-gram word-based LM. As a part of this research, a new dataset of Qur'an verses and their associated transcripts were collected and processed for training and evaluating the proposed model, consisting of 10 h of .wav recitations performed by 60 reciters. The experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate (CER) of 1.98% and a word error rate (WER) of 6.16%. We have achieved state-of-the-art end-to-end transformer-based recognition for Qur'an reciters.

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“…As findings of a previously funded research project [7], two baseline HMM-based systems for phonemes and allophones [8,9] were constructed using the mentioned speech database. The number of allophones in the speech database is 110 plus a silence unit which is counted as normal allophone indicating short pauses during the recitations, while the number of phonemes is 60, which represents almost half of the number of allophones.…”

Section: Research Efforts Summarymentioning

confidence: 99%

Helpful Statistics in Recognizing Basic Arabic Phonemes

Khelifa¹,

Yousfi²,

Elhadj³

et al. 2017

ijacsa

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Abstract-The recognition of continuous speech is one of the main challenges in the building of automatic speech recognition (ASR) systems, especially when it comes to phonetically complex languages such as Arabic. An ASR system seems to be actually in a blocked alley. Nearly all solutions follow the same general model. The previous research focused on enhancing its performance by incorporating supplementary features. This paper is part of ongoing research efforts aimed at developing a high-performance Arabic speech recognition system for learning and teaching purposes. It investigates a statistical analysis of certain distinctive features of the basic Arabic phonemes which seems helpful in enhancing the performance of a baseline HMMbased ASR system. The statistics are collected using a particular Arabic speech database, which involves ten different male speakers and more than eight hours of speech which covers all Arabic phonemes. In HMM modeling framework, the statistics provided are helpful in establishing the appropriate number of HMM states for each phoneme and they can also be utilized as an initial condition for the EM estimation procedure, which generally, accelerates the estimation process and, thus, improves the performance of the system. The obtained findings are presented and possible applications of automatic speech recognition and speaker identification systems are also suggested.

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Approach for Recognizing Allophonic Sounds of the Classical Arabic Based on Quran Recitations

Cited by 9 publications

References 3 publications

Discriminative Training for Phonetic Recognition of the Holy Quran

Discriminative Training for Phonetic Recognition of the Holy Quran

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

Helpful Statistics in Recognizing Basic Arabic Phonemes

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