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Talk · Procedia Computer Science · January 1, 2024

Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers

Raghad Salameh, Mohamad Al Mdfaa, Nursultan Askarbekuly, Manuel Mazzara

Manuel Mazzara
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Abstract

In this paper, we address the challenge of learning to recite the Quran for non-Arabic speakers. We explore the possibility of crowdsourcing a carefully annotated Quranic dataset, on top of which AI models can be built to simplify the learning process. In particular, we use the volunteer-based crowdsourcing genre and implement a crowdsourcing API to gather audio assets. We integrated the API into an existing mobile application called NamazApp for collecting audio recitations. We developed a crowd-sourcing platform called Quran Voice for annotating the gathered audio assets. As a result, we have collected around 7000 Quranic recitations from a pool of 1287 participants across more than 11 non-Arabic countries, and we have annotated 1166 recitations from the dataset in six categories. We have achieved a crowd accuracy of 0.77, an inter-rater agreement of 0.63 between the annotators, and 0.89 between the labels assigned by the algorithm and the expert judgments. 1

Mobile Crowdsensing and CrowdsourcingSpeech Recognition and SynthesisMusic and Audio ProcessingQuranic recitationnon-native Arabic speakerscrowdsourcing

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