Towards an accurate speaker-independent Holy Quran acoustic model

Amrani, Mohamed Yassine El; Rahman, M. M. Hafizur; Wahiddin, Mohamed Ridza; Shah, Asadullah

doi:10.1109/icetas.2017.8277887

Cited by 3 publications

(3 citation statements)

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“…Second, only a few studies follow traditional speech recognition, as explained in Fig. 2, either with a deep learning architecture like [49], [50], [3] or without, such as [34], [21]. It is worth noting that more work investigating deep learning architecture should be conducted to improve the accuracy of Arabic speech recognition in general and the Holy Quran in particular.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…The researchers constructed an acoustic model using the Carnegie Melon University (CMU) Sphinx trainer [21]. The CMU Sphinx trainer utilized recordings from 39 different reciters and 49 chapters (surah) to build a robust framework for continuous speech recognition.…”

Section: B Traditional Based Speech Recognitionmentioning

confidence: 99%

“…In [21], the CMU Sphinx trainer [53] was employed to train the acoustic model specifically for the Holy Quran. In a similar vein, a study by [22] utilized the Jaro-Winkler algorithm for text matching and relied on the Google Speech API to establish a framework for speech recognition.…”

Section: Hqsr Taxonomymentioning

confidence: 99%

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Speech Recognition Models for Holy Quran Recitation Based on Modern Approaches and Tajweed Rules: A Comprehensive Overview

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Speech is considered the most natural way to communicate with people. The purpose of speech recognition technology is to allow machines to recognize and understand human speech, enabling them to take action based on the spoken words. Speech recognition is especially useful in educational fields, as it can provide powerful automatic correction for language learning purposes. In the context of learning the Quran, it is essential for every Muslim to recite it correctly. Traditionally, this involves an expert gari who listens to the student's recitation, identifies any mistakes, and provides appropriate corrections. While effective, this method is time-consuming. To address this challenge, apps that help students fix their recitation of the Holy Quran are becoming increasingly popular. However, these apps require a robust and error-free speech recognition model. While recent advancements in speech recognition have produced highly accurate results for written and spoken Arabic and non-Arabic speech recognition, the field of Holy Quran speech recognition is still in its early stages. Therefore, this paper aims to provide a comprehensive literature review of the existing research in the field of Holy Quran speech recognition. Its goal is to identify the limitations of current works, determine future research directions, and highlight important research in the fields of spoken and written languages.

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